Covid19Japan.com

Covid19 Japanが独自に収集している陽性者単位のデータ（個票データ）。ソースとデータは全てGitHubにて公開されており、データはJSON形式。「レコード数 \(\neq\) 累計陽性者数」であることに注意。

　

Import

Covid19 JapanがGitHubで公開しているデータは前述のようにJSON形式であり、最新データはlatest.jsonファイルで示されている。このため、読み込む際はひと工夫必要。

個票データ（Patient Data）

陽性者単位の個票データ。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/patient_data/"

df <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df

　

集計データ（Summary Data）

死亡者数や重症者数などの推移データはsummaryフォルダ内のJSON形式ファイルにまとめられている。読み込むと分かるがリスト型で、その中データフレームが含まれる形式である。
summaryフォルダの他にsummary_minフォルダというフォルダがあるが、summary_minフォルダ内のJSONファイルは単に改行を省略して小さくしたファイル。

path <- "https://raw.githubusercontent.com/reustle/covid19japan-data/master/docs/summary/"

df_s <- path %>% 
  paste0("latest.json") %>% 
  readr::read_lines() %>% 
  paste0(path, .) %>% 
  jsonlite::fromJSON()

df_s %>% summary()

##             Length Class      Mode     
## prefectures 27     data.frame list     
## regions     12     data.frame list     
## daily       37     data.frame list     
## updated      1     -none-     character

　
三つのデータフレームと一つのベクトル（更新日時）から構成されている。データフレームは上から順に都道府県別、地方別、日次となっているが、Lengthを見てわかるようにそれぞれに含まれる集計データが異なっている。

　

都道府県単位集計

更新日時（$updated）における都道府県単位での累積値。厚生労働省がオープンデータから除いている空港検疫・ダイヤモンドプリンセス・長崎クルーズ船・その他が含まれるので全51区分になっている。

df_s$prefectures

陽性者・死亡者などの時系列集計データがネストされて格納されている。日付はネストされていないので、各項目に対するstartDateの項を参照すること。

項目	内容	備考
dailyConfirmedCount	陽性者数	単日
dailyConfirmedStartDate	陽性者数のカウント開始日	区分により開始日が異なる
dailyDeceasedCount	死亡者数	単日
dailyDeceasedStartDate	死亡者数のカウント開始日	区分により開始日が異なる
dailyRecoveredCumulative	快復者数	累計
dailyRecoveredStartDate	快復者数のカウント開始日	区分により開始日が異なる
dailyActive	治療者数1	単日
dailyActiveStartDate	治療者数のカウント開始日	区分により開始日が異なる

¹ 陽性者数から死亡者数と快復者数を引いた数値を治療者数としている

　

地方単位集計

更新日次時点における地方区分単位での累積値。陽性者の時系列集計データが都道府県単位データと同様にネストで格納されているが、死亡者・快復者・治療者のデータは含まれていない。
なお、時系列データの合計値と累積項の値が一致しない場合がある。

df_s$regions

df_s$regions$dailyConfirmedCount[[1]] %>% sum()

## [1] 63608

　

日次集計

個票データを日次で集計したもの。日付を見れば分かる通り暗黙の欠落を含んでいる。

df_s$daily

　

更新日時

集計データの更新日時。

df_s$updated

## [1] "2020-11-12T22:09:59+09:00"

　

Area Data

地域・地方ごとの分析を行う場合に便利な都道府県データを用意した。このデータはGistで公開している。

　

Others

その他のオープンデータ。

• 新型コロナウイルス対策病床オープンデータ

　

Data Wrangling

Summarize

最初に個票データの内容を確認する。これには要約に便利なskimrパッケージを用いる。

df %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	115383
Number of columns	23
_______________________
Column type frequency:
character	19
logical	3
numeric	1
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	113656	0
dateAnnounced	0	1.00	10	10	0	289	0
gender	20571	0.82	1	1	0	2	0
detectedPrefecture	0	1.00	3	15	0	49	0
patientStatus	111360	0.03	8	23	0	8	0
notes	60068	0.48	1	270	0	52517	1
mhlwPatientNumber	114934	0.00	1	11	0	434	0
prefecturePatientNumber	18184	0.84	5	20	0	97190	0
prefectureSourceURL	84064	0.27	5	224	0	3441	0
residence	28620	0.75	1	38	0	1423	0
sourceURL	637	0.99	1	239	0	8354	0
relatedPatients	104539	0.09	2	259	0	6549	0
knownCluster	112884	0.02	3	88	0	231	0
detectedCityTown	88783	0.23	2	22	0	663	0
cityPrefectureNumber	89044	0.23	1	34	0	26330	2
citySourceURL	103481	0.10	9	317	0	3654	0
deceasedDate	113519	0.02	10	10	0	239	0
deceasedReportedDate	114163	0.01	10	62	0	207	0
deathSourceURL	114308	0.01	14	123	0	656	0

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	0.99	TRU: 113655, FAL: 1728
charterFlightPassenger	115369	0	1.00	TRU: 14
cruisePassengerDisembarked	115372	0	1.00	TRU: 11

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
ageBracket	0	1	31.02	24.07	-1	20	30	50	100	▅▇▅▂▁

　
元がJSON形式なので、読み込んだ直後は殆どの変量（フィーチャー）が文字型になっていることが分かる。また、意外と欠損が多いことも分かる。

　

Tidy & Transform

各変量（フィーチャー）を適切な形式に変換し、地域区分でも分析できるように都道府県データと結合することで、ベースとなるデータセットを作成する。

x <- df %>% 
  dplyr::select(patientId, date = dateAnnounced, gender,
                pref = detectedPrefecture, patientStatus, knownCluster,
                confirmedPatient, charterFlightPassenger,
                cruisePassengerDisembarked, ageBracket,
                deceasedDate, deceasedReportedDate) %>% 
  dplyr::filter(confirmedPatient == TRUE) %>% 
  dplyr::mutate(date = lubridate::as_date(date),
                gender = forcats::as_factor(gender),
                patientStatus = forcats::as_factor(patientStatus),
                cluster = dplyr::if_else(!is.na(knownCluster), TRUE, FALSE),
                ageBracket = forcats::as_factor(ageBracket),
                deceasedDate = lubridate::as_date(deceasedDate),
                deceasedReportedDate = lubridate::as_date(deceasedReportedDate)) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::select(-`推計人口`) %>% 
  dplyr::rename(Pref = `都道府県`, region = `八地方区分`)

# %>%
#   dplyr::filter(date != dplyr::if_else(lubridate::hour(lubridate::now()) < 17L,
#                                        max(date), max(date) + 1))

x

変換結果を要約してみると

x %>% 
  skimr::skim()

Data summary

Name	Piped data
Number of rows	113655
Number of columns	19
_______________________
Column type frequency:
character	3
Date	3
factor	9
logical	4
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
patientId	0	1.00	1	8	0	113655	0
pref	0	1.00	3	15	0	49	0
knownCluster	111185	0.02	3	88	0	229	0

Variable type: Date

skim_variable	n_missing	complete_rate	min	max	median	n_unique
date	0	1	2020-01-15	2020-11-12	2020-08-18	289
deceasedDate	113276	0	2020-02-13	2020-10-17	2020-05-08	150
deceasedReportedDate	113325	0	2020-02-13	2020-10-17	2020-05-16	131

Variable type: factor

skim_variable	n_missing	complete_rate	ordered	n_unique	top_counts
gender	19868	0.83	FALSE	2	M: 52675, F: 41112
patientStatus	111122	0.02	FALSE	8	Hos: 1261, Dec: 371, Hom: 315, Dis: 283
ageBracket	0	1.00	FALSE	13	20: 25601, -1: 19967, 30: 16290, 40: 13559
pcode	1328	0.99	FALSE	47	13: 33808, 27: 14607, 14: 9766, 23: 7252
Pref	1328	0.99	FALSE	47	東京都: 33808, 大阪府: 14607, 神奈川: 9766, 愛知県: 7252
region	1328	0.99	FALSE	8	関東地: 58034, 近畿地: 22922, 九州地: 11444, 中部地: 11263
広域圏	9761	0.91	FALSE	8	首都圏: 58309, 近畿圏: 22317, 中部圏: 9853, 九州圏: 7820
通俗的区分	1328	0.99	FALSE	11	関東: 58034, 関西: 22317, 東海: 9448, 九州: 7820
fct_pref	1328	0.99	FALSE	47	Tok: 33808, Osa: 14607, Kan: 9766, Aic: 7252

Variable type: logical

skim_variable	n_missing	complete_rate	mean	count
confirmedPatient	0	1	1.00	TRU: 113655
charterFlightPassenger	113641	0	1.00	TRU: 14
cruisePassengerDisembarked	113644	0	1.00	TRU: 11
cluster	0	1	0.02	FAL: 111185, TRU: 2470

　
文字型を因子型に変換するだけでも大まかな傾向が見えるようになる。例えば

• 年齢別で見ると20代、30代、年齢不明（恐らく非回答）、40代の順に多い
• 都道府県別では東京、大阪、神奈川、愛知の順と人口にほぼ比例
• 地方区分で見ると関東、近畿、九州、中部となっており九州地方が以外と多い

ことが読める。

patientStatusは以下の通りで、ほぼ更新されていないのと思われる。死者数などの推移を見る場合はサマリデータを使った方がいい。

x %>% 
  dplyr::group_by(patientStatus) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(Japanese = c("回復", "入院中", "退院済", "死亡", "詳細不明",
                             "重症", "自宅療養", "ホテル療養", NA))

　

集計（陽性者）

　

地方別集計

地方別で陽性者数と人口千人あたりの比率を比較する。

region <- prefs %>% 
  dplyr::group_by(`八地方区分`) %>% 
  dplyr::summarise(population = sum(`推計人口`)) %>% 
  dplyr::rename(region = `八地方区分`)

r_by_region <- x %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(region, by = c("region" = "region")) %>% 
  dplyr::select(region, n, population) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_region

　

都道府県別集計

同様に都道府県別に比較する。

r_by_prefs <- x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2))
r_by_prefs %>% 
  tibble::rowid_to_column("No") %>% 
  DT::datatable()

　

累計陽性者数トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = n, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

人口千人あたりの比率トップ10

x %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::left_join(prefs, by = c("Pref" = "都道府県")) %>% 
  dplyr::select(Pref, n, population = `推計人口`) %>% 
  dplyr::mutate(rate = round(n / population, 2)) %>% 
  dplyr::slice_max(order_by = rate, n = 10) %>% 
  dplyr::rename(`累計陽性者数` = n, `推計人口[千人]` = population, `率` = rate)

　

クラスタ比率

x %>% 
  dplyr::group_by(region, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1))

　

x %>% 
  dplyr::group_by(Pref, cluster) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::drop_na() %>% 
  tidyr::pivot_wider(names_from = cluster, values_from = n) %>%
  dplyr::mutate(ratio = (`TRUE` / (`TRUE` + `FALSE`) * 100) %>% round(1)) %>% 
  tidyr::replace_na(list(`TRUE` = 0L, ratio = 0.0)) %>% 
  tibble::rowid_to_column(var = "No") %>% 
  DT::datatable()

　

日次集計（陽性者）

　

全国日次集計

日次単位の陽性者数、前日差、累計、移動平均（7日）を求める。

x_by_all <- x %>% 
  dplyr::group_by(date) %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::summarise(n = n()) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day"),
                  fill = list(n = 0L)) %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n),
                ma7 = zoo::rollmeanr(n, k = 7L, na.pad = TRUE))

x_by_all

　

地方区分別日次集計

同様に地方区分別の集計を求める。

x_by_region <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, region) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = region, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "region", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs %>% dplyr::distinct(`八地方区分`), .,
                   by = c("八地方区分" = "region")) %>% 
  dplyr::mutate(region = forcats::fct_inorder(`八地方区分`)) %>% 
  dplyr::select(date, region, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_region

　

都道府県別日次集計

同様に都道府県別の集計を求める。

x_by_prefs <- x %>% 
  dplyr::filter(!is.na(Pref)) %>% 
  dplyr::group_by(date, Pref) %>% 
  dplyr::summarise(n = n()) %>% 
  dplyr::ungroup() %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = n, values_fill = 0L) %>% 
  tidyr::complete(date = seq.Date(from = min(date), to = max(date), by = "day")) %>% 
  tidyr::pivot_longer(cols = -date, names_to = "Pref", values_to = "n") %>% 
  tidyr::replace_na(replace = list(n = 0L)) %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
x_by_prefs

　

日次集計（死亡者）

　

都道府県別

都道府県別の日次単位の死亡者数、前日差、累計、移動平均（7日）を求める。

start <- df_s$prefectures %>% 
  dplyr::select(pref = name, date = dailyDeceasedStartDate) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  dplyr::arrange(pcode) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(date, Pref = `都道府県`) %>% 
  dplyr::distinct(date) %>% 
  .$date %>% lubridate::as_date()

d_by_prefs <- df_s$prefectures %>% 
  dplyr::select(deceased = dailyDeceasedCount, pref = name) %>% 
  dplyr::left_join(prefs, by = c("pref" = "pref")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::select(Pref = `都道府県`, deceased) %>% 
  tidyr::unnest(deceased) %>% 
  tidyr::pivot_wider(names_from = Pref, values_from = deceased) %>% 
  tidyr::unnest() %>% 
  dplyr::mutate(date = seq.Date(from = start, to = start + nrow(.) - 1,
                                by = "day")) %>% 
  dplyr::select(date, dplyr::everything()) %>% 
  tidyr::pivot_longer(col = -date, names_to = "Pref", values_to = "n") %>% 
  dplyr::group_by(Pref) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::left_join(prefs, ., by = c("都道府県" = "Pref")) %>% 
  dplyr::mutate(Pref = forcats::fct_inorder(`都道府県`)) %>% 
  dplyr::select(date, Pref, n, diff, cum, ma7) %>% 
  dplyr::arrange(date)
d_by_prefs

　

地方別

集計データ$regionsには死亡者数の日次データが存在しないため$prefecturesのデータから計算する。

d_by_region <- d_by_prefs %>% 
  dplyr::select(date, pref = Pref, n) %>% 
  dplyr::left_join(prefs, by = c("pref" = "都道府県")) %>% 
  tidyr::drop_na(pcode) %>% 
  dplyr::group_by(date, `八地方区分`) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::rename(region = `八地方区分`) %>% 
  dplyr::group_by(region) %>% 
  tidyr::nest() %>% 
  dplyr::mutate(diff = purrr::map(data, ~ lagdiff(.$n)),
                cum = purrr::map(data, ~ cumsum(.$n)),
                ma7 = purrr::map(data, ~ ma7(.$n))) %>% 
  tidyr::unnest() %>% 
  dplyr::arrange(date)
d_by_region

　

陽性者比率と死亡者比率

rpd_by_all <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population) %>% 
  dplyr::select(-region) %>% 
  dplyr::summarise_all(sum) %>% 
  dplyr::mutate(p_rate = round(positive / population, 2),
                d_rate = round(deceased / positive, 2))

rpd_by_all %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_region <- d_by_region %>% 
  dplyr::group_by(region) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_region, ., by = c("region")) %>% 
  dplyr::select(region, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2))

rpd_by_region %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

rpd_by_prefs <- d_by_prefs %>% 
  dplyr::group_by(Pref) %>% 
  dplyr::summarise(d = sum(n)) %>% 
  dplyr::left_join(r_by_prefs, ., by = "Pref") %>% 
  dplyr::select(Pref, positive = n, deceased = d, population, p_rate = rate) %>% 
  dplyr::mutate(d_rate = round(deceased / positive, 2)) 

rpd_by_prefs %>% 
  dplyr::rename(`陽性者数` = positive, `死亡者数` = deceased,
                `推計人口` = population, `人口千人あたりの陽性者比率` = p_rate,
                `陽性者に対する死亡者比率` = d_rate)

　

全国日次集計

都道府県別のデータから全国の日次集計を求める。

d_by_all <- d_by_prefs %>% 
  dplyr::group_by(date) %>% 
  dplyr::summarise(n = sum(n)) %>% 
  dplyr::ungroup() %>% 
  dplyr::mutate(diff = lagdiff(n), cum = cumsum(n), ma7 = ma7(n))
d_by_all

　

Visualize

　

陽性者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の陽性者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の陽性者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

陽性者数（単日）

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = n)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      width = 1.0, alpha = 0.5) + 
    ggplot2::labs(title = paste0("地方別陽性者数推移（単日） @", datetime),
                  caption = caption, 
                  x = "", y = "陽性者数") 

　

移動平均

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = ma7, colour = region)) + 
    ggplot2::geom_line(size = 1) +
    ggplot2::theme(legend.position = 'none') +
    ggplot2::labs(title = paste0("地方別7日間移動平均 @", datetime),
                  caption = caption,
                  x = "", y = "陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)),
                             nudge_x = 30, segment.alpha = 0.5, size = 3) + 
    ggplot2::lims(x = c(min(x_by_region$date),
                        max(x_by_region$date) + 45))

　

累積

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date, y = cum, colour = region)) + 
    ggplot2::geom_line() +
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("地方別累積陽性者数 @", datetime),
                  caption = caption,
                  x = "", y = "累積陽性者数") + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region),
                             data = subset(x_by_region, date == max(date)))

　

単日＋累積

sec_scale <- 50
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.5, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

前日差

x_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = region)) +
    ggplot2::facet_wrap(~ region, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  caption = caption, x = "", y = "")

　

都道府県別

　

単日＋累計

sec_scale <- 100
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積陽性者数（折線）")
    )

　

前日差

x_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数前日差, Free Y scale @", datetime),
                  x = "", y = "")

　

死亡者の日次推移

　

全国

sec_scale <- 100
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n), stat = "identity", width = 1.0,
                      alpha = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7), linetype = "dashed",
                       colour = "dark green", size = 0.5) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale),
                       colour = "dark green", size = 1.0) +
    ggplot2::labs(title = paste0("全国の死亡者数推移（単日） @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") +
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（破線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_all %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_line(ggplot2::aes(y = diff), colour = "dark green", alpha = 0.5) + 
    ggplot2::labs(title = paste0("全国の死亡者数前日差 @", datetime),
                  subtitle = subtitle, caption = caption, 
                  x = "", y = "前日差")

　

地方別

sec_scale <- 50
ncol <- 4
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = region), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = region),
                       linetype = "solid", size = 0.2) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = region)) +
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数・移動平均（細線）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

都道府県別日次推移

sec_scale <- 10
ncol <- 5
datetime <- lubridate::as_datetime(df_s$updated, tz = "Japan")


d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.25, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Fixed scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

d_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
  ggplot2::geom_bar(ggplot2::aes(y = n, fill = Pref), stat = "identity",
                      alpha = 0.35, width = 1.0) + 
    ggplot2::geom_line(ggplot2::aes(y = ma7, colour = Pref),
                       linetype = "solid", size = 0.25) + 
    ggplot2::geom_line(ggplot2::aes(y = cum / sec_scale, colour = Pref)) +
    ggplot2::facet_wrap(~ Pref, ncol = ncol, scales = "free_y") + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "死亡者数（単日）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "累積死亡者数（折線）")
    )

　

比較

陽性者数と死亡者の比較。

　

全国

sec_scale <- (1 / 50)

x_by_all %>% 
  dplyr::left_join(d_by_all, by = c("date")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

地方別

sec_scale <- (1 / 10)
ncol <- 4

x_by_region %>% 
  dplyr::left_join(d_by_region, by = c("date" = "date", "region" = "region")) %>% 
  ggplot2::ggplot(ggplot2::aes(x = date)) + 
    ggplot2::geom_bar(ggplot2::aes(y = n.x), stat = "identity",
                      fill = "dark green", alpha = 0.25, width = 1.0) +
    ggplot2::geom_bar(ggplot2::aes(y = n.y / sec_scale), stat = "identity",
                      fill = "dark red", alpha = 0.25, width = 1.0) +
    # ggplot2::geom_line(ggplot2::aes(y = n.x), colour = "dark green") + 
    # ggplot2::geom_line(ggplot2::aes(y = n.y / sec_scale), colour = "dark red") + 
    ggplot2::facet_wrap(~ region, ncol = ncol, scales = "free_y") + 
    ggplot2::labs(title = paste0("Free Y scale @", datetime), caption = caption,
                  x = "", y = "") + 
    ggplot2::scale_y_continuous(
      name = "陽性者数（濃緑）",
      sec.axis = ggplot2::sec_axis(~ . * sec_scale,
                                    name = "死亡者数（濃赤）")
    )

　

相関

　

地方区分別

r_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("推計人口と陽性者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_region %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_point(ggplot2::aes(colour = region)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = region, colour = region)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

都道府県別

r_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("@", datetime), caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

r_by_prefs %>% 
  dplyr::filter(n < 5000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = population, y = n) ) + 
    ggplot2::geom_abline(slope = 1, intercept = 0, colour = "gray") + 
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("累計陽性者数五千人未満 @", datetime),
                  caption = caption,
                  x = "推計人口[千人]", y = "累計陽性者数")

　

rpd_by_prefs %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

rpd_by_prefs %>% 
  dplyr::filter(positive < 1000) %>% 
  ggplot2::ggplot(ggplot2::aes(x = positive, y = deceased)) + 
    ggplot2::geom_abline(slope = rpd_by_all$d_rate, intercept = 0, colour = "gray") +
    ggplot2::geom_point(ggplot2::aes(colour = Pref)) + 
    ggrepel::geom_text_repel(ggplot2::aes(label = Pref, colour = Pref)) + 
    ggplot2::theme(legend.position = 'none') + 
    ggplot2::labs(title = paste0("陽性者数と死亡者数 @", datetime),
                  subtitle = subtitle, caption = caption,
                  x = "陽性者数", y = "死亡者数")

　

Model

時系列（TS）分析

日本の時系列データは週単位の変動が認められるので、frequencyを7に設定して陽性者数のデータをtsオブジェクトに変換する。

ts_week <- x_by_all %>% 
  dplyr::select(n) %>% 
  ts(frequency = 7)

時系列データに変換したものをプロットすると可視化の項でプロットした棒グラフと同じような形のグラフになることが分かります。

ts_week %>% 
  plot(main = paste0("全国 @", datetime))

上記からトレンド（長期的傾向）を除いたグラフ。デフォルト指定なのでlag = 1。つまり、前日差。

ts_week %>% 
    base::diff() %>% 
  plot(main = paste0("全国 @", datetime))

トレンド、季節変動（周期変動）、非周期変動に分解した場合。frequency = 1では分解できない点に注意。

ts_week %>% 
  stats::decompose() %>% 
  plot()

トレンドを抜き出してみる。移動平均に酷似している。

ts_week %>% 
  stats::decompose() %>% 
  .$x %>% 
  plot(ylim = c(0, 1500), main = paste0("全国 @", datetime))

par(new = TRUE)

ts_week %>% 
  stats::decompose() %>% 
  .$trend %>% 
  plot(ylim = c(0, 1500), col = "dark green", lwd = 3)

　

地方別時系列分析

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

oldpar <- par()
par(mfrow=c(4, 2))
x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道地方
## NULL
## 
## $東北地方
## NULL
## 
## $関東地方
## NULL
## 
## $中部地方
## NULL
## 
## $近畿地方
## NULL
## 
## $中国地方
## NULL
## 
## $四国地方
## NULL
## 
## $九州地方
## NULL

par(oldpar)

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name, ylim = c(0, max(.x)))
                # plot(.x, main = region)
                par(new = TRUE)
                stats::decompose(.x) %>% 
                  .$trend %>% 
                  plot(ylim = c(0, max(.x)), col = "dark green", lwd = 3)
                  # plot(col = "dark green", lwd = 2)
              } )

## $北海道
## NULL
## 
## $青森県
## NULL
## 
## $岩手県
## NULL
## 
## $宮城県
## NULL
## 
## $秋田県
## NULL
## 
## $山形県
## NULL
## 
## $福島県
## NULL
## 
## $茨城県
## NULL
## 
## $栃木県
## NULL
## 
## $群馬県
## NULL
## 
## $埼玉県
## NULL
## 
## $千葉県
## NULL
## 
## $東京都
## NULL
## 
## $神奈川県
## NULL
## 
## $新潟県
## NULL
## 
## $富山県
## NULL
## 
## $石川県
## NULL
## 
## $福井県
## NULL
## 
## $山梨県
## NULL
## 
## $長野県
## NULL
## 
## $岐阜県
## NULL
## 
## $静岡県
## NULL
## 
## $愛知県
## NULL
## 
## $三重県
## NULL
## 
## $滋賀県
## NULL
## 
## $京都府
## NULL
## 
## $大阪府
## NULL
## 
## $兵庫県
## NULL
## 
## $奈良県
## NULL
## 
## $和歌山県
## NULL
## 
## $鳥取県
## NULL
## 
## $島根県
## NULL
## 
## $岡山県
## NULL
## 
## $広島県
## NULL
## 
## $山口県
## NULL
## 
## $徳島県
## NULL
## 
## $香川県
## NULL
## 
## $愛媛県
## NULL
## 
## $高知県
## NULL
## 
## $福岡県
## NULL
## 
## $佐賀県
## NULL
## 
## $長崎県
## NULL
## 
## $熊本県
## NULL
## 
## $大分県
## NULL
## 
## $宮崎県
## NULL
## 
## $鹿児島県
## NULL
## 
## $沖縄県
## NULL

　

Infer

時系列予測（ARIMA）

ARIMA（Auto Regressive Integrated Moving Average, 自己回帰和分移動平均）モデルによる陽性者に対する予測。予測に必要なパラメータはステップワイズにより自動的に最適なものが選択される。ただし、モデル自体を評価していないので、こういうことが出来る程度の話。

　

全国

x_by_all %>% 
  dplyr::select(n) %>% 
  ts(.$n, frequency = 7) %>% 
  forecast::auto.arima() %>%  
  forecast::forecast() %>% 
  plot(main = paste0("全国 @", datetime))

　

地方別

x_by_region %>% 
  dplyr::select(region, n) %>% 
  split(.$region) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道地方
## $北海道地方$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 235.3390 284.1750 273.4578 307.1199 306.6217 327.5329 356.5436 365.3169
##  [9] 400.7400 405.3794 429.2136 440.3666 462.9869 482.1054
## 
## $北海道地方$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 225.9058 220.9122
## 44.42857 273.8232 268.3433
## 44.57143 260.0257 252.9151
## 44.71429 292.6806 285.0370
## 44.85714 290.4412 281.8758
## 45.00000 309.0949 299.3345
## 45.14286 335.7891 324.8023
## 45.28571 340.4767 327.3271
## 45.42857 373.1380 358.5264
## 45.57143 373.7638 357.0276
## 45.71429 394.4512 376.0491
## 45.85714 402.2658 382.0965
## 46.00000 421.2413 399.1425
## 46.14286 436.4847 412.3346
## 
## $北海道地方$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 244.7721 249.7657
## 44.42857 294.5268 300.0067
## 44.57143 286.8900 294.0005
## 44.71429 321.5592 329.2029
## 44.85714 322.8021 331.3676
## 45.00000 345.9708 355.7312
## 45.14286 377.2981 388.2849
## 45.28571 390.1572 403.3068
## 45.42857 428.3419 442.9535
## 45.57143 436.9950 453.7313
## 45.71429 463.9761 482.3782
## 45.85714 478.4674 498.6368
## 46.00000 504.7325 526.8313
## 46.14286 527.7261 551.8762
## 
## 
## $東北地方
## $東北地方$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 27.27605 25.92297 26.71635 23.95706 27.19497 29.37754 25.74092 27.73016
##  [9] 27.59922 27.89637 28.72624 27.12052 25.27916 27.54411
## 
## $東北地方$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 20.48570 16.89110
## 44.42857 18.97141 15.29148
## 44.57143 19.60725 15.84391
## 44.71429 16.69383 12.84890
## 44.85714 19.78081 15.85598
## 45.00000 21.81545 17.81233
## 45.14286 18.03376 13.95383
## 45.28571 19.70114 15.45084
## 45.42857 19.40132 15.06162
## 45.57143 19.53301 15.10571
## 45.71429 20.20062 15.68742
## 45.85714 18.43566 13.83818
## 46.00000 16.43794 11.75768
## 46.14286 18.54924 13.78765
## 
## $東北地方$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 34.06639 37.66099
## 44.42857 32.87452 36.55445
## 44.57143 33.82545 37.58878
## 44.71429 31.22030 35.06523
## 44.85714 34.60914 38.53397
## 45.00000 36.93963 40.94275
## 45.14286 33.44809 37.52802
## 45.28571 35.75917 40.00947
## 45.42857 35.79711 40.13681
## 45.57143 36.25974 40.68704
## 45.71429 37.25187 41.76506
## 45.85714 35.80537 40.40286
## 46.00000 34.12037 38.80063
## 46.14286 36.53897 41.30056
## 
## 
## $関東地方
## $関東地方$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 685.7039 667.8289 522.5066 428.1986 613.3507 640.9687 737.3199 701.4965
##  [9] 703.4935 539.9888 445.8411 611.5915 645.5744 737.3440
## 
## $関東地方$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 622.3330 588.7864
## 44.42857 588.0664 545.8426
## 44.57143 434.4658 387.8598
## 44.71429 337.1099 288.8905
## 44.85714 518.9412 468.9638
## 45.00000 542.6643 490.6251
## 45.14286 632.3011 576.7075
## 45.28571 583.9611 521.7416
## 45.42857 575.7402 508.1117
## 45.57143 405.8925 334.9062
## 45.71429 307.5465 234.3378
## 45.85714 470.1747 395.3132
## 46.00000 500.7336 424.0596
## 46.14286 588.5487 509.7812
## 
## $関東地方$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 749.0748 782.6214
## 44.42857 747.5915 789.8153
## 44.57143 610.5474 657.1533
## 44.71429 519.2873 567.5067
## 44.85714 707.7602 757.7376
## 45.00000 739.2731 791.3124
## 45.14286 842.3387 897.9323
## 45.28571 819.0319 881.2514
## 45.42857 831.2467 898.8752
## 45.57143 674.0851 745.0715
## 45.71429 584.1357 657.3445
## 45.85714 753.0084 827.8699
## 46.00000 790.4151 867.0892
## 46.14286 886.1393 964.9068
## 
## 
## $中部地方
## $中部地方$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 227.6761 226.9843 218.3654 189.8557 242.4887 245.3581 250.9132 275.6453
##  [9] 271.2840 262.8008 255.1546 275.1721 285.1891 284.4242
## 
## $中部地方$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 209.6700 200.1381
## 44.42857 204.3597 192.3830
## 44.57143 192.0974 178.1919
## 44.71429 160.0119 144.2136
## 44.85714 210.5066 193.5762
## 45.00000 210.8448 192.5745
## 45.14286 213.6147 193.8701
## 45.28571 232.7335 210.0173
## 45.42857 222.3957 196.5158
## 45.57143 210.5434 182.8800
## 45.71429 198.2372 168.1069
## 45.85714 215.1425 183.3648
## 46.00000 221.8166 188.2693
## 46.14286 217.4967 182.0674
## 
## $中部地方$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 245.6822 255.2141
## 44.42857 249.6090 261.5857
## 44.57143 244.6335 258.5390
## 44.71429 219.6995 235.4979
## 44.85714 274.4708 291.4011
## 45.00000 279.8714 298.1417
## 45.14286 288.2116 307.9562
## 45.28571 318.5571 341.2733
## 45.42857 320.1724 346.0523
## 45.57143 315.0582 342.7216
## 45.71429 312.0721 342.2024
## 45.85714 335.2016 366.9794
## 46.00000 348.5616 382.1090
## 46.14286 351.3517 386.7810
## 
## 
## $近畿地方
## $近畿地方$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 378.9031 397.1902 345.2261 301.5395 415.2142 416.2934 429.6155 459.2122
##  [9] 463.2947 422.1814 376.7921 486.8196 501.2386 506.7873
## 
## $近畿地方$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 346.3295 329.0861
## 44.42857 358.8071 338.4884
## 44.57143 304.0123 282.1951
## 44.71429 256.1000 232.0458
## 44.85714 367.0664 341.5785
## 45.00000 364.7401 337.4495
## 45.14286 375.4943 346.8443
## 45.28571 396.8817 363.8859
## 45.42857 396.1879 360.6636
## 45.57143 351.0161 313.3434
## 45.71429 301.4932 261.6325
## 45.85714 407.8215 366.0024
## 46.00000 418.5441 374.7683
## 46.14286 420.6766 375.0923
## 
## $近畿地方$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 411.4768 428.7202
## 44.42857 435.5733 455.8921
## 44.57143 386.4399 408.2571
## 44.71429 346.9789 371.0331
## 44.85714 463.3619 488.8498
## 45.00000 467.8466 495.1372
## 45.14286 483.7367 512.3868
## 45.28571 521.5427 554.5384
## 45.42857 530.4015 565.9257
## 45.57143 493.3467 531.0193
## 45.71429 452.0909 491.9517
## 45.85714 565.8177 607.6368
## 46.00000 583.9332 627.7090
## 46.14286 592.8980 638.4823
## 
## 
## $中国地方
## $中国地方$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 10.60797 10.82355 10.82355 10.82355 10.82355 10.82355 10.82355 10.82355
##  [9] 10.82355 10.82355 10.82355 10.82355 10.82355 10.82355
## 
## $中国地方$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                  80%       95%
## 44.28571  1.63924777 -3.108508
## 44.42857  1.45157414 -3.509650
## 44.57143  1.26632234 -3.792968
## 44.71429  1.08459371 -4.070898
## 44.85714  0.90619457 -4.343736
## 45.00000  0.73094836 -4.611752
## 45.14286  0.55869360 -4.875193
## 45.28571  0.38928213 -5.134285
## 45.42857  0.22257764 -5.389238
## 45.57143  0.05845437 -5.640243
## 45.71429 -0.10320400 -5.887478
## 45.85714 -0.26250530 -6.131108
## 46.00000 -0.41954971 -6.371287
## 46.14286 -0.57443053 -6.608157
## 
## $中国地方$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 19.57670 24.32445
## 44.42857 20.19552 25.15675
## 44.57143 20.38078 25.44007
## 44.71429 20.56250 25.71800
## 44.85714 20.74090 25.99083
## 45.00000 20.91615 26.25885
## 45.14286 21.08840 26.52229
## 45.28571 21.25782 26.78138
## 45.42857 21.42452 27.03634
## 45.57143 21.58864 27.28734
## 45.71429 21.75030 27.53458
## 45.85714 21.90960 27.77821
## 46.00000 22.06665 28.01839
## 46.14286 22.22153 28.25525
## 
## 
## $四国地方
## $四国地方$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 3.716618 2.774351 2.774351 2.774351 2.774351 2.774351 2.774351 2.774351
##  [9] 2.774351 2.774351 2.774351 2.774351 2.774351 2.774351
## 
## $四国地方$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571  0.2303474 -1.615173
## 44.42857 -0.9665286 -2.946831
## 44.57143 -1.0397217 -3.058770
## 44.71429 -1.1115365 -3.168601
## 44.85714 -1.1820479 -3.276439
## 45.00000 -1.2513244 -3.382389
## 45.14286 -1.3194289 -3.486545
## 45.28571 -1.3864187 -3.588997
## 45.42857 -1.4523469 -3.689826
## 45.57143 -1.5172624 -3.789106
## 45.71429 -1.5812105 -3.886906
## 45.85714 -1.6442333 -3.983291
## 46.00000 -1.7063697 -4.078320
## 46.14286 -1.7676561 -4.172050
## 
## $四国地方$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 7.202888 9.048409
## 44.42857 6.515231 8.495534
## 44.57143 6.588425 8.607473
## 44.71429 6.660239 8.717304
## 44.85714 6.730751 8.825142
## 45.00000 6.800027 8.931091
## 45.14286 6.868132 9.035248
## 45.28571 6.935122 9.137700
## 45.42857 7.001050 9.238529
## 45.57143 7.065965 9.337808
## 45.71429 7.129913 9.435609
## 45.85714 7.192936 9.531994
## 46.00000 7.255073 9.627023
## 46.14286 7.316359 9.720753
## 
## 
## $九州地方
## $九州地方$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 53.99756 46.71047 56.78888 49.07063 53.73909 56.20684 53.92978 54.59765
##  [9] 50.28897 54.15876 50.81848 53.21148 56.32347 51.20164
## 
## $九州地方$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%         95%
## 44.28571  32.048171  20.4288678
## 44.42857  20.156390   6.0995076
## 44.57143  27.082500  11.3568940
## 44.71429  18.424977   2.2021509
## 44.85714  21.049112   3.7440820
## 45.00000  19.310760  -0.2208469
## 45.14286  13.260294  -8.2688321
## 45.28571   7.746865 -17.0544429
## 45.42857  -1.174832 -28.4181258
## 45.57143  -1.364505 -30.7567476
## 45.71429  -7.936654 -39.0397430
## 45.85714  -8.950640 -41.8572809
## 46.00000  -9.584399 -44.4739150
## 46.14286 -18.250241 -55.0158456
## 
## $九州地方$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571  75.94694  87.56624
## 44.42857  73.26456  87.32144
## 44.57143  86.49525 102.22086
## 44.71429  79.71627  95.93910
## 44.85714  86.42907 103.73410
## 45.00000  93.10293 112.63453
## 45.14286  94.59927 116.12839
## 45.28571 101.44844 126.24975
## 45.42857 101.75278 128.99607
## 45.57143 109.68203 139.07427
## 45.71429 109.57361 140.67670
## 45.85714 115.37361 148.28025
## 46.00000 122.23133 157.12085
## 46.14286 120.65352 157.41912

　

都道府県別

x_by_prefs %>% 
  dplyr::select(Pref, n) %>% 
  split(.$Pref) %>% 
  purrr::map(., ~ ts(.$n, frequency = 7)) %>% 
  purrr::map(., forecast::auto.arima) %>% 
  purrr::map(., forecast::forecast) %>% 
  purrr::map2(., paste0(names(.), " @", datetime),
              function(.x, name) {
                plot(.x, main = name)
              } )

## $北海道
## $北海道$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 235.3390 284.1750 273.4578 307.1199 306.6217 327.5329 356.5436 365.3169
##  [9] 400.7400 405.3794 429.2136 440.3666 462.9869 482.1054
## 
## $北海道$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 225.9058 220.9122
## 44.42857 273.8232 268.3433
## 44.57143 260.0257 252.9151
## 44.71429 292.6806 285.0370
## 44.85714 290.4412 281.8758
## 45.00000 309.0949 299.3345
## 45.14286 335.7891 324.8023
## 45.28571 340.4767 327.3271
## 45.42857 373.1380 358.5264
## 45.57143 373.7638 357.0276
## 45.71429 394.4512 376.0491
## 45.85714 402.2658 382.0965
## 46.00000 421.2413 399.1425
## 46.14286 436.4847 412.3346
## 
## $北海道$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 244.7721 249.7657
## 44.42857 294.5268 300.0067
## 44.57143 286.8900 294.0005
## 44.71429 321.5592 329.2029
## 44.85714 322.8021 331.3676
## 45.00000 345.9708 355.7312
## 45.14286 377.2981 388.2849
## 45.28571 390.1572 403.3068
## 45.42857 428.3419 442.9535
## 45.57143 436.9950 453.7313
## 45.71429 463.9761 482.3782
## 45.85714 478.4674 498.6368
## 46.00000 504.7325 526.8313
## 46.14286 527.7261 551.8762
## 
## 
## $青森県
## $青森県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.6635868 0.8807234 1.3715190 1.0638276 1.2567266 1.1357937 1.2116094
##  [8] 1.1640787 1.1938768 1.1751957 1.1869074 1.1795650 1.1841681 1.1812823
## 
## $青森県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -0.8828076 -2.230787
## 44.42857 -2.1131519 -3.698014
## 44.57143 -1.7000376 -3.326021
## 44.71429 -2.2497819 -4.003901
## 44.85714 -2.1754004 -3.992259
## 45.00000 -2.4722818 -4.382282
## 45.14286 -2.5260852 -4.504701
## 45.28571 -2.7215051 -4.778409
## 45.42857 -2.8202937 -4.945267
## 45.57143 -2.9719300 -5.167286
## 45.71429 -3.0839281 -5.344772
## 45.85714 -3.2146030 -5.540735
## 46.00000 -3.3280639 -5.716695
## 46.14286 -3.4471623 -5.897313
## 
## $青森県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 4.209981 5.557961
## 44.42857 3.874599 5.459461
## 44.57143 4.443076 6.069059
## 44.71429 4.377437 6.131556
## 44.85714 4.688854 6.505712
## 45.00000 4.743869 6.653869
## 45.14286 4.949304 6.927920
## 45.28571 5.049663 7.106566
## 45.42857 5.208047 7.333021
## 45.57143 5.322321 7.517677
## 45.71429 5.457743 7.718587
## 45.85714 5.573733 7.899865
## 46.00000 5.696400 8.085032
## 46.14286 5.809727 8.259878
## 
## 
## $岩手県
## $岩手県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.673604 1.408526 1.408766 1.658104 1.784068 3.403471 1.659067 1.760025
##  [9] 1.760025 1.760025 1.760025 1.760025 1.760025 1.760025
## 
## $岩手県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571 0.8545395 0.4209527
## 44.42857 0.5825637 0.1453259
## 44.57143 0.5759641 0.1351055
## 44.71429 0.8185182 0.3740683
## 44.85714 0.9377520 0.4897396
## 45.00000 2.5504790 2.0989322
## 45.14286 0.7994504 0.3443967
## 45.28571 0.8459676 0.3620949
## 45.42857 0.8363315 0.3473576
## 45.57143 0.8267948 0.3327726
## 45.71429 0.8173546 0.3183351
## 45.85714 0.8080080 0.3040407
## 46.00000 0.7987523 0.2898853
## 46.14286 0.7895849 0.2758649
## 
## $岩手県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.492669 2.926256
## 44.42857 2.234487 2.671725
## 44.57143 2.241568 2.682426
## 44.71429 2.497690 2.942140
## 44.85714 2.630383 3.078395
## 45.00000 4.256463 4.708010
## 45.14286 2.518684 2.973738
## 45.28571 2.674082 3.157955
## 45.42857 2.683719 3.172692
## 45.57143 2.693255 3.187277
## 45.71429 2.702695 3.201715
## 45.85714 2.712042 3.216009
## 46.00000 2.721298 3.230165
## 46.14286 2.730465 3.244185
## 
## 
## $宮城県
## $宮城県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 21.47608 19.15577 20.99500 19.53710 20.69273 19.77670 20.50281 19.92725
##  [9] 20.38348 20.02184 20.30850 20.08127 20.26139 20.11862
## 
## $宮城県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 16.74581 14.241761
## 44.42857 14.37103 11.838150
## 44.57143 15.87299 13.161556
## 44.71429 14.32780 11.570159
## 44.85714 15.22962 12.337620
## 45.00000 14.20513 11.255718
## 45.14286 14.72461 11.665809
## 45.28571 14.02885 10.906426
## 45.42857 14.30719 11.090593
## 45.57143 13.81998 10.536918
## 45.71429 13.94741 10.580056
## 45.85714 13.59318 10.158593
## 46.00000 13.62691 10.114834
## 46.14286 13.35798  9.779121
## 
## $宮城県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 26.20635 28.71040
## 44.42857 23.94050 26.47338
## 44.57143 26.11702 28.82845
## 44.71429 24.74640 27.50404
## 44.85714 26.15585 29.04785
## 45.00000 25.34827 28.29768
## 45.14286 26.28102 29.33981
## 45.28571 25.82564 28.94807
## 45.42857 26.45977 29.67636
## 45.57143 26.22369 29.50676
## 45.71429 26.66958 30.03694
## 45.85714 26.56936 30.00395
## 46.00000 26.89586 30.40794
## 46.14286 26.87925 30.45811
## 
## 
## $秋田県
## $秋田県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 0.5935881 0.4299633 0.4299633 0.4299633 0.4299633 0.4299633 0.4299633
##  [8] 0.4299633 0.4299633 0.4299633 0.4299633 0.4299633 0.4299633 0.4299633
## 
## $秋田県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -0.6415475 -1.295389
## 44.42857 -0.8181855 -1.478916
## 44.57143 -0.8196531 -1.481160
## 44.71429 -0.8211189 -1.483402
## 44.85714 -0.8225831 -1.485641
## 45.00000 -0.8240455 -1.487878
## 45.14286 -0.8255062 -1.490112
## 45.28571 -0.8269652 -1.492343
## 45.42857 -0.8284226 -1.494572
## 45.57143 -0.8298782 -1.496798
## 45.71429 -0.8313322 -1.499022
## 45.85714 -0.8327845 -1.501243
## 46.00000 -0.8342351 -1.503461
## 46.14286 -0.8356841 -1.505677
## 
## $秋田県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 1.828724 2.482565
## 44.42857 1.678112 2.338842
## 44.57143 1.679580 2.341087
## 44.71429 1.681046 2.343329
## 44.85714 1.682510 2.345568
## 45.00000 1.683972 2.347804
## 45.14286 1.685433 2.350038
## 45.28571 1.686892 2.352270
## 45.42857 1.688349 2.354498
## 45.57143 1.689805 2.356725
## 45.71429 1.691259 2.358948
## 45.85714 1.692711 2.361169
## 46.00000 1.694162 2.363388
## 46.14286 1.695611 2.365604
## 
## 
## $山形県
## $山形県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.648206 1.831440 1.757629 1.824894 1.885419 1.482228 1.662211 1.689273
##  [9] 1.582691 1.470137 1.354503 1.238311 1.123713 1.012517
## 
## $山形県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                  80%        95%
## 44.28571  0.70629485  0.2076775
## 44.42857  0.86769371  0.3575172
## 44.57143  0.76208422  0.2350744
## 44.71429  0.78963560  0.2416027
## 44.85714  0.80525808  0.2334552
## 45.00000  0.35471683 -0.2421516
## 45.14286  0.48728102 -0.1346892
## 45.28571  0.50628043 -0.1199578
## 45.42857  0.36629950 -0.2776194
## 45.57143  0.22562466 -0.4331801
## 45.71429  0.08680994 -0.5842664
## 45.85714 -0.04807766 -0.7290505
## 46.00000 -0.17740112 -0.8661694
## 46.14286 -0.29990061 -0.9946523
## 
## $山形県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.590116 3.088734
## 44.42857 2.795187 3.305363
## 44.57143 2.753175 3.280184
## 44.71429 2.860153 3.408186
## 44.85714 2.965581 3.537384
## 45.00000 2.609740 3.206608
## 45.14286 2.837141 3.459111
## 45.28571 2.872265 3.498503
## 45.42857 2.799083 3.443002
## 45.57143 2.714648 3.373453
## 45.71429 2.622197 3.293273
## 45.85714 2.524699 3.205672
## 46.00000 2.424827 3.113596
## 46.14286 2.324934 3.019686
## 
## 
## $福島県
## $福島県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.540519 1.747907 1.757091 1.899811 2.833410 1.650877 2.593402 1.869744
##  [9] 2.022281 1.963251 2.076334 1.604139 1.858580 1.892905
## 
## $福島県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                  80%       95%
## 44.28571 -0.98519711 -2.322230
## 44.42857 -0.85032284 -2.225743
## 44.57143 -0.91168268 -2.324446
## 44.71429 -0.83768908 -2.286834
## 44.85714  0.02886678 -1.455769
## 45.00000 -1.21914353 -2.738441
## 45.14286 -0.34063519 -1.893821
## 45.28571 -1.17990666 -2.794294
## 45.42857 -1.09864349 -2.750761
## 45.57143 -1.22735584 -2.916361
## 45.71429 -1.18246518 -2.907569
## 45.85714 -1.72145532 -3.481918
## 46.00000 -1.53249298 -3.327619
## 46.14286 -1.56240668 -3.391538
## 
## $福島県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 4.066235 5.403269
## 44.42857 4.346136 5.721556
## 44.57143 4.425864 5.838628
## 44.71429 4.637311 6.086457
## 44.85714 5.637953 7.122589
## 45.00000 4.520898 6.040195
## 45.14286 5.527439 7.080624
## 45.28571 4.919394 6.533781
## 45.42857 5.143205 6.795323
## 45.57143 5.153858 6.842863
## 45.71429 5.335134 7.060238
## 45.85714 4.929733 6.690196
## 46.00000 5.249654 7.044779
## 46.14286 5.348217 7.177349
## 
## 
## $茨城県
## $茨城県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 17.31074 16.53641 16.64297 16.08974 16.28391 17.71228 18.83654 17.55359
##  [9] 17.71288 17.90020 17.46993 17.65441 18.51782 18.95390
## 
## $茨城県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 13.66881 11.740883
## 44.42857 12.62995 10.562000
## 44.57143 12.57735 10.425146
## 44.71429 11.87096  9.637675
## 44.85714 11.91734  9.605821
## 45.00000 13.20276 10.815565
## 45.14286 14.18846 11.727923
## 45.28571 12.64383 10.044753
## 45.42857 12.63315  9.944104
## 45.57143 12.66480  9.893347
## 45.71429 12.08336  9.231884
## 45.85714 12.12079  9.191475
## 46.00000 12.84097  9.835828
## 46.14286 13.13734 10.058234
## 
## $茨城県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 20.95267 22.88059
## 44.42857 20.44287 22.51082
## 44.57143 20.70859 22.86080
## 44.71429 20.30852 22.54180
## 44.85714 20.65048 22.96200
## 45.00000 22.22180 24.60899
## 45.14286 23.48461 25.94515
## 45.28571 22.46336 25.06243
## 45.42857 22.79260 25.48165
## 45.57143 23.13559 25.90704
## 45.71429 22.85650 25.70798
## 45.85714 23.18802 26.11734
## 46.00000 24.19468 27.19982
## 46.14286 24.77046 27.84957
## 
## 
## $栃木県
## $栃木県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.888898 1.904626 1.608907 1.756367 2.300720 1.958695 1.959587 1.907038
##  [9] 1.907038 1.907038 1.907038 1.907038 1.907038 1.907038
## 
## $栃木県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -1.0941447 -2.673272
## 44.42857 -1.1212545 -2.723059
## 44.57143 -1.4592129 -3.083377
## 44.71429 -1.3534184 -2.999640
## 44.85714 -0.8501813 -2.518167
## 45.00000 -1.2327910 -2.922262
## 45.14286 -1.2719754 -2.982661
## 45.28571 -1.4967728 -3.298641
## 45.42857 -1.5497535 -3.379668
## 45.57143 -1.6019344 -3.459472
## 45.71429 -1.6533505 -3.538106
## 45.85714 -1.7040347 -3.615621
## 46.00000 -1.7540173 -3.692063
## 46.14286 -1.8033265 -3.767475
## 
## $栃木県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 4.871940 6.451068
## 44.42857 4.930506 6.532310
## 44.57143 4.677027 6.301191
## 44.71429 4.866153 6.512374
## 44.85714 5.451621 7.119607
## 45.00000 5.150182 6.839653
## 45.14286 5.191149 6.901834
## 45.28571 5.310848 7.112717
## 45.42857 5.363829 7.193744
## 45.57143 5.416010 7.273548
## 45.71429 5.467426 7.352182
## 45.85714 5.518110 7.429697
## 46.00000 5.568093 7.506138
## 46.14286 5.617402 7.581550
## 
## 
## $群馬県
## $群馬県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 5.199845 5.550405 5.021506 5.249638 5.489942 5.601175 5.574170 5.653369
##  [9] 5.648022 5.706147 5.691687 5.734044 5.724413 5.755516
## 
## $群馬県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571  0.7306940 -1.635131
## 44.42857  0.4318967 -2.277678
## 44.57143 -0.2977170 -3.113544
## 44.71429 -0.1329756 -2.982359
## 44.85714 -0.2113372 -3.229412
## 45.00000 -0.1973191 -3.266857
## 45.14286 -0.3352765 -3.463549
## 45.28571 -0.3624175 -3.546983
## 45.42857 -0.5078129 -3.766515
## 45.57143 -0.5002541 -3.785725
## 45.71429 -0.5800243 -3.900068
## 45.85714 -0.5798135 -3.922168
## 46.00000 -0.6444288 -4.015890
## 46.14286 -0.6435387 -4.030993
## 
## $群馬県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%      95%
## 44.28571  9.668996 12.03482
## 44.42857 10.668913 13.37849
## 44.57143 10.340729 13.15656
## 44.71429 10.632251 13.48163
## 44.85714 11.191220 14.20930
## 45.00000 11.399670 14.46921
## 45.14286 11.483617 14.61189
## 45.28571 11.669156 14.85372
## 45.42857 11.803858 15.06256
## 45.57143 11.912549 15.19802
## 45.71429 11.963399 15.28344
## 45.85714 12.047902 15.39026
## 46.00000 12.093254 15.46472
## 46.14286 12.154570 15.54202
## 
## 
## $埼玉県
## $埼玉県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 108.46859  88.78089  78.03949  79.36004  85.77976 106.73462  90.47019
##  [8] 116.13317 100.52795  92.01391  93.06062  98.14914 114.75876 101.86695
## 
## $埼玉県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 94.91658 87.74258
## 44.42857 74.52577 66.97956
## 44.57143 63.11435 55.21345
## 44.71429 63.79368 55.55335
## 44.85714 69.59758 61.03125
## 45.00000 89.95921 81.07885
## 45.14286 73.12182 63.93815
## 45.28571 96.50431 86.11342
## 45.42857 80.00978 69.14812
## 45.57143 70.64342 59.33056
## 45.71429 70.87052 59.12378
## 45.85714 75.16863 63.00349
## 46.00000 91.01415 78.44452
## 46.14286 77.38207 64.42056
## 
## $埼玉県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%      95%
## 44.28571 122.02060 129.1946
## 44.42857 103.03601 110.5822
## 44.57143  92.96464 100.8655
## 44.71429  94.92639 103.1667
## 44.85714 101.96194 110.5283
## 45.00000 123.51004 132.3904
## 45.14286 107.81856 117.0022
## 45.28571 135.76203 146.1529
## 45.42857 121.04611 131.9078
## 45.57143 113.38440 124.6973
## 45.71429 115.25073 126.9975
## 45.85714 121.12964 133.2948
## 46.00000 138.50337 151.0730
## 46.14286 126.35183 139.3133
## 
## 
## $千葉県
## $千葉県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 57.47133 59.29469 54.86669 52.28913 56.40652 59.78071 63.58425 59.17522
##  [9] 60.62591 59.01006 57.76879 59.07051 61.35193 61.61297
## 
## $千葉県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 45.93740 39.83171
## 44.42857 46.39848 39.57165
## 44.57143 41.55707 34.51138
## 44.71429 38.57855 31.32061
## 44.85714 42.30638 34.84221
## 45.00000 45.30148 37.63664
## 45.14286 48.73561 40.87521
## 45.28571 43.20448 34.75008
## 45.42857 44.00963 35.21349
## 45.57143 41.91196 32.86077
## 45.71429 40.20208 30.90283
## 45.85714 41.04737 31.50650
## 46.00000 42.88365 33.10713
## 46.14286 42.71002 32.70341
## 
## $千葉県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 69.00526 75.11095
## 44.42857 72.19089 79.01772
## 44.57143 68.17631 75.22200
## 44.71429 65.99971 73.25766
## 44.85714 70.50667 77.97083
## 45.00000 74.25994 81.92478
## 45.14286 78.43289 86.29329
## 45.28571 75.14597 83.60037
## 45.42857 77.24220 86.03833
## 45.57143 76.10816 85.15935
## 45.71429 75.33550 84.63475
## 45.85714 77.09364 86.63451
## 46.00000 79.82021 89.59673
## 46.14286 80.51593 90.52254
## 
## 
## $東京都
## $東京都$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 335.6449 361.0441 259.3438 218.3012 319.8197 305.7822 389.4956 348.5358
##  [9] 376.6364 274.4354 232.9082 333.9578 319.4663 402.7403
## 
## $東京都$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 290.0922 265.9781
## 44.42857 308.3876 280.5129
## 44.57143 203.5412 174.0011
## 44.71429 159.7044 128.6851
## 44.85714 258.7211 226.3774
## 45.00000 242.4294 208.8925
## 45.14286 324.1015 289.4840
## 45.28571 276.0780 237.7211
## 45.42857 300.2496 259.8129
## 45.57143 195.2171 153.2814
## 45.71429 151.1261 107.8333
## 45.85714 249.8448 205.3181
## 46.00000 233.2269 187.5745
## 46.14286 314.5553 267.8730
## 
## $東京都$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 381.1976 405.3117
## 44.42857 413.7006 441.5753
## 44.57143 315.1463 344.6864
## 44.71429 276.8980 307.9172
## 44.85714 380.9184 413.2620
## 45.00000 369.1351 402.6720
## 45.14286 454.8897 489.5072
## 45.28571 420.9937 459.3506
## 45.42857 453.0233 493.4600
## 45.57143 353.6538 395.5895
## 45.71429 314.6903 357.9831
## 45.85714 418.0707 462.5974
## 46.00000 405.7057 451.3581
## 46.14286 490.9252 537.6075
## 
## 
## $神奈川県
## $神奈川県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 120.05753 127.57970  99.52860  81.75308 105.86782 120.29274 126.87992
##  [8] 117.44609 119.75614 106.51194 100.67994 109.41134 118.54592 115.60407
## 
## $神奈川県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%      95%
## 44.28571  99.91799 89.25676
## 44.42857 105.08349 93.17472
## 44.57143  76.21590 63.87491
## 44.71429  57.98975 45.41021
## 44.85714  81.76646 69.00797
## 45.00000  95.89410 82.97824
## 45.14286 102.20082 89.13649
## 45.28571  89.85438 75.24820
## 45.42857  91.03505 75.83103
## 45.57143  77.11040 61.54617
## 45.71429  70.74282 54.89506
## 45.85714  78.99360 62.89143
## 46.00000  87.67186 71.32812
## 46.14286  84.28665 67.70820
## 
## $神奈川県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 140.1971 150.8583
## 44.42857 150.0759 161.9847
## 44.57143 122.8413 135.1823
## 44.71429 105.5164 118.0959
## 44.85714 129.9692 142.7277
## 45.00000 144.6914 157.6072
## 45.14286 151.5590 164.6233
## 45.28571 145.0378 159.6440
## 45.42857 148.4772 163.6813
## 45.57143 135.9135 151.4777
## 45.71429 130.6171 146.4648
## 45.85714 139.8291 155.9312
## 46.00000 149.4200 165.7637
## 46.14286 146.9215 163.4999
## 
## 
## $新潟県
## $新潟県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 4.5741522 5.7214373 4.4343452 2.6927544 1.9147354 4.1602007 1.4730215
##  [8] 1.6672489 1.7756703 1.3129910 1.1019608 0.9759627 0.9007340 0.8558178
## 
## $新潟県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                  80%        95%
## 44.28571  2.83060154  1.9076216
## 44.42857  3.95384812  3.0181429
## 44.57143  2.61158129  1.6466684
## 44.71429  0.76150929 -0.2608301
## 44.85714 -0.05373644 -1.0957825
## 45.00000  2.17862720  1.1296456
## 45.14286 -0.51320163 -1.5646446
## 45.28571 -0.35980384 -1.4328608
## 45.42857 -0.25396509 -1.3283892
## 45.57143 -0.71970710 -1.7957525
## 45.71429 -0.93526432 -2.0137062
## 45.85714 -1.06287382 -2.1421687
## 46.00000 -1.13867663 -2.2182754
## 46.14286 -1.18379745 -2.2635045
## 
## $新潟県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 6.317703 7.240683
## 44.42857 7.489026 8.424732
## 44.57143 6.257109 7.222022
## 44.71429 4.623999 5.646339
## 44.85714 3.883207 4.925253
## 45.00000 6.141774 7.190756
## 45.14286 3.459245 4.510688
## 45.28571 3.694302 4.767359
## 45.42857 3.805306 4.879730
## 45.57143 3.345689 4.421734
## 45.71429 3.139186 4.217628
## 45.85714 3.014799 4.094094
## 46.00000 2.940145 4.019743
## 46.14286 2.895433 3.975140
## 
## 
## $富山県
## $富山県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 0.12108228 0.21868521 0.10568786 0.10719687 0.11177389 0.11736512
##  [7] 0.10057404 0.09713489 0.09310894 0.08980949 0.08424850 0.08040590
## [13] 0.07654590 0.07297993
## 
## $富山県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571 -2.345454 -3.651159
## 44.42857 -2.386495 -3.765594
## 44.57143 -2.658343 -4.121533
## 44.71429 -2.778672 -4.306358
## 44.85714 -2.962786 -4.590360
## 45.00000 -3.068822 -4.755487
## 45.14286 -3.190218 -4.932258
## 45.28571 -3.282979 -5.072304
## 45.42857 -3.372487 -5.207063
## 45.57143 -3.447356 -5.319819
## 45.71429 -3.517362 -5.423940
## 45.85714 -3.578240 -5.515010
## 46.00000 -3.633571 -5.597588
## 46.14286 -3.682778 -5.670956
## 
## $富山県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.587618 3.893323
## 44.42857 2.823866 4.202965
## 44.57143 2.869719 4.332909
## 44.71429 2.993065 4.520752
## 44.85714 3.186334 4.813908
## 45.00000 3.303552 4.990217
## 45.14286 3.391366 5.133406
## 45.28571 3.477249 5.266573
## 45.42857 3.558705 5.393281
## 45.57143 3.626975 5.499438
## 45.71429 3.685859 5.592437
## 45.85714 3.739051 5.675821
## 46.00000 3.786662 5.750680
## 46.14286 3.828738 5.816916
## 
## 
## $石川県
## $石川県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.568906 1.568906 1.568906 1.568906 1.568906 1.568906 1.568906 1.568906
##  [9] 1.568906 1.568906 1.568906 1.568906 1.568906 1.568906
## 
## $石川県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571 -2.123606 -4.078303
## 44.42857 -2.331167 -4.395741
## 44.57143 -2.528227 -4.697118
## 44.71429 -2.716234 -4.984650
## 44.85714 -2.896332 -5.260086
## 45.00000 -3.069443 -5.524836
## 45.14286 -3.236321 -5.780054
## 45.28571 -3.397595 -6.026702
## 45.42857 -3.553794 -6.265588
## 45.57143 -3.705370 -6.497403
## 45.71429 -3.852709 -6.722739
## 45.85714 -3.996149 -6.942112
## 46.00000 -4.135984 -7.155970
## 46.14286 -4.272472 -7.364711
## 
## $石川県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 5.261418  7.216116
## 44.42857 5.468979  7.533553
## 44.57143 5.666039  7.834930
## 44.71429 5.854046  8.122462
## 44.85714 6.034144  8.397899
## 45.00000 6.207255  8.662648
## 45.14286 6.374133  8.917867
## 45.28571 6.535407  9.164514
## 45.42857 6.691607  9.403401
## 45.57143 6.843182  9.635215
## 45.71429 6.990522  9.860552
## 45.85714 7.133962 10.079924
## 46.00000 7.273796 10.293783
## 46.14286 7.410284 10.502523
## 
## 
## $福井県
## $福井県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.7208096 0.8690596 1.1585760 1.2984565 0.8978203 1.1336216 1.0811390
##  [8] 0.9310863 1.0613703 0.9805969 0.9418236 0.9957773 0.9364697 0.9358618
## 
## $福井県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -0.1151094 -1.086986
## 44.42857 -1.0771549 -2.107419
## 44.57143 -0.9899987 -2.127385
## 44.71429 -1.0935034 -2.359730
## 44.85714 -1.5383454 -2.827974
## 45.00000 -1.4026344 -2.745247
## 45.14286 -1.5262466 -2.906513
## 45.28571 -1.7011110 -3.094512
## 45.42857 -1.6166402 -3.034293
## 45.57143 -1.7218201 -3.152393
## 45.71429 -1.7749596 -3.213138
## 45.85714 -1.7405554 -3.189082
## 46.00000 -1.8094824 -3.263102
## 46.14286 -1.8179235 -3.275689
## 
## $福井県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 3.556729 4.528605
## 44.42857 2.815274 3.845538
## 44.57143 3.307151 4.444537
## 44.71429 3.690416 4.956643
## 44.85714 3.333986 4.623614
## 45.00000 3.669878 5.012490
## 45.14286 3.688525 5.068791
## 45.28571 3.563284 4.956685
## 45.42857 3.739381 5.157034
## 45.57143 3.683014 5.113587
## 45.71429 3.658607 5.096785
## 45.85714 3.732110 5.180637
## 46.00000 3.682422 5.136041
## 46.14286 3.689647 5.147413
## 
## 
## $山梨県
## $山梨県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 7.876539 7.908992 7.908992 7.908992 7.908992 7.908992 7.908992 7.908992
##  [9] 7.908992 7.908992 7.908992 7.908992 7.908992 7.908992
## 
## $山梨県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 6.151951 5.239009
## 44.42857 6.024652 5.027143
## 44.57143 5.935270 4.890445
## 44.71429 5.849764 4.759674
## 44.85714 5.767669 4.634122
## 45.00000 5.688608 4.513208
## 45.14286 5.612267 4.396454
## 45.28571 5.538382 4.283458
## 45.42857 5.466732 4.173878
## 45.57143 5.397125 4.067424
## 45.71429 5.329396 3.963840
## 45.85714 5.263400 3.862908
## 46.00000 5.199010 3.764433
## 46.14286 5.136116 3.668244
## 
## $山梨県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%      95%
## 44.28571  9.601128 10.51407
## 44.42857  9.793331 10.79084
## 44.57143  9.882714 10.92754
## 44.71429  9.968220 11.05831
## 44.85714 10.050314 11.18386
## 45.00000 10.129376 11.30478
## 45.14286 10.205717 11.42153
## 45.28571 10.279601 11.53453
## 45.42857 10.351251 11.64411
## 45.57143 10.420858 11.75056
## 45.71429 10.488588 11.85414
## 45.85714 10.554584 11.95508
## 46.00000 10.618973 12.05355
## 46.14286 10.681868 12.14974
## 
## 
## $長野県
## $長野県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 7.811873 8.635816 8.635816 8.635816 8.635816 8.635816 8.635816 8.635816
##  [9] 8.635816 8.635816 8.635816 8.635816 8.635816 8.635816
## 
## $長野県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 5.035979 3.566510
## 44.42857 5.790952 4.284972
## 44.57143 5.627823 4.035487
## 44.71429 5.473096 3.798853
## 44.85714 5.325594 3.573268
## 45.00000 5.184390 3.357315
## 45.14286 5.048740 3.149856
## 45.28571 4.918036 2.949962
## 45.42857 4.791773 2.756860
## 45.57143 4.669528 2.569903
## 45.71429 4.550940 2.388538
## 45.85714 4.435699 2.212291
## 46.00000 4.323536 2.040753
## 46.14286 4.214218 1.873565
## 
## $長野県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 10.58777 12.05724
## 44.42857 11.48068 12.98666
## 44.57143 11.64381 13.23614
## 44.71429 11.79854 13.47278
## 44.85714 11.94604 13.69836
## 45.00000 12.08724 13.91432
## 45.14286 12.22289 14.12178
## 45.28571 12.35360 14.32167
## 45.42857 12.47986 14.51477
## 45.57143 12.60210 14.70173
## 45.71429 12.72069 14.88309
## 45.85714 12.83593 15.05934
## 46.00000 12.94810 15.23088
## 46.14286 13.05741 15.39807
## 
## 
## $岐阜県
## $岐阜県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 17.42186 13.68317 13.55323 13.87008 15.22168 14.02809 17.01325 16.68001
##  [9] 14.59568 14.96172 14.81019 15.65176 15.29239 16.39131
## 
## $岐阜県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571 14.076389 12.305405
## 44.42857  9.867993  7.848359
## 44.57143  9.344249  7.116146
## 44.71429  9.394574  7.025387
## 44.85714 10.527836  8.043063
## 45.00000  9.148204  6.564946
## 45.14286 11.966392  9.294744
## 45.28571 11.175018  8.260851
## 45.42857  8.817922  5.759364
## 45.57143  8.931530  5.739342
## 45.71429  8.559166  5.250076
## 45.85714  9.196190  5.778818
## 46.00000  8.644706  5.125635
## 46.14286  9.560413  5.944352
## 
## $岐阜県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 20.76733 22.53831
## 44.42857 17.49835 19.51799
## 44.57143 17.76222 19.99032
## 44.71429 18.34558 20.71476
## 44.85714 19.91553 22.40030
## 45.00000 18.90799 21.49124
## 45.14286 22.06012 24.73177
## 45.28571 22.18500 25.09917
## 45.42857 20.37343 23.43199
## 45.57143 20.99190 24.18409
## 45.71429 21.06121 24.37030
## 45.85714 22.10733 25.52470
## 46.00000 21.94007 25.45914
## 46.14286 23.22222 26.83828
## 
## 
## $静岡県
## $静岡県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 12.48533 14.82321 13.65081 12.08611 15.75846 13.68352 14.07345 16.05057
##  [9] 18.39695 14.71630 13.68104 15.58351 15.86720 14.59765
## 
## $静岡県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%      95%
## 44.28571  8.631381 6.591224
## 44.42857 10.347442 7.978115
## 44.57143  8.927162 6.426614
## 44.71429  7.214034 4.634911
## 44.85714 10.514307 7.738222
## 45.00000  8.191990 5.284950
## 45.14286  8.421153 5.429006
## 45.28571 10.188331 7.085050
## 45.42857 12.335413 9.126631
## 45.57143  8.503251 5.214263
## 45.71429  7.265464 3.869261
## 45.85714  8.982270 5.487783
## 46.00000  9.113864 5.538865
## 46.14286  7.669386 4.001787
## 
## $静岡県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 16.33928 18.37943
## 44.42857 19.29897 21.66830
## 44.57143 18.37446 20.87501
## 44.71429 16.95819 19.53731
## 44.85714 21.00260 23.77869
## 45.00000 19.17505 22.08209
## 45.14286 19.72575 22.71790
## 45.28571 21.91281 25.01609
## 45.42857 24.45848 27.66726
## 45.57143 20.92934 24.21833
## 45.71429 20.09662 23.49282
## 45.85714 22.18476 25.67924
## 46.00000 22.62053 26.19553
## 46.14286 21.52591 25.19351
## 
## 
## $愛知県
## $愛知県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 142.9399 147.9098 135.3531 120.7563 138.6722 137.4495 157.5260 160.1715
##  [9] 159.9115 152.2309 143.1573 139.4815 143.6877 152.3042
## 
## $愛知県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 128.4950 120.84842
## 44.42857 130.6609 121.52986
## 44.57143 115.3831 104.81158
## 44.71429  98.5548  86.80201
## 44.85714 114.8441 102.23031
## 45.00000 112.3999  99.13948
## 45.14286 131.3475 117.48953
## 45.28571 130.6284 114.98917
## 45.42857 127.6974 110.64423
## 45.57143 117.2628  98.75177
## 45.71429 105.8027  86.02837
## 45.85714 100.2927  79.54744
## 46.00000 103.0569  81.54831
## 46.14286 110.3135  88.08493
## 
## $愛知県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 157.3847 165.0313
## 44.42857 165.1586 174.2897
## 44.57143 155.3231 165.8946
## 44.71429 142.9579 154.7107
## 44.85714 162.5002 175.1140
## 45.00000 162.4990 175.7594
## 45.14286 183.7044 197.5624
## 45.28571 189.7146 205.3538
## 45.42857 192.1257 209.1788
## 45.57143 187.1991 205.7101
## 45.71429 180.5118 200.2862
## 45.85714 178.6702 199.4155
## 46.00000 184.3185 205.8271
## 46.14286 194.2949 216.5235
## 
## 
## $三重県
## $三重県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 3.175203 3.175203 3.175203 3.175203 3.175203 3.175203 3.175203 3.175203
##  [9] 3.175203 3.175203 3.175203 3.175203 3.175203 3.175203
## 
## $三重県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -0.5288864 -2.489713
## 44.42857 -0.7341408 -2.803623
## 44.57143 -0.9291434 -3.101853
## 44.71429 -1.1152923 -3.386544
## 44.85714 -1.2936940 -3.659385
## 45.00000 -1.4652421 -3.921746
## 45.14286 -1.6306706 -4.174747
## 45.28571 -1.7905912 -4.419324
## 45.42857 -1.9455198 -4.656267
## 45.57143 -2.0958967 -4.886249
## 45.71429 -2.2421010 -5.109849
## 45.85714 -2.3844619 -5.327571
## 46.00000 -2.5232673 -5.539856
## 46.14286 -2.6587711 -5.747091
## 
## $三重県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 6.879293  8.840120
## 44.42857 7.084548  9.154029
## 44.57143 7.279550  9.452260
## 44.71429 7.465699  9.736950
## 44.85714 7.644101 10.009792
## 45.00000 7.815649 10.272152
## 45.14286 7.981077 10.525153
## 45.28571 8.140998 10.769731
## 45.42857 8.295927 11.006674
## 45.57143 8.446304 11.236655
## 45.71429 8.592508 11.460256
## 45.85714 8.734869 11.677978
## 46.00000 8.873674 11.890262
## 46.14286 9.009178 12.097498
## 
## 
## $滋賀県
## $滋賀県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 9.626305 9.587988 9.131085 9.105626 8.953302 8.940605 8.889705 8.884072
##  [9] 8.867024 8.864680 8.858957 8.858020 8.856095 8.855730
## 
## $滋賀県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 5.769403 3.7276819
## 44.42857 5.665421 3.5889389
## 44.57143 4.769562 2.4607105
## 44.71429 4.652716 2.2954875
## 44.85714 4.324206 1.8737104
## 45.00000 4.216983 1.7164482
## 45.14286 4.050109 1.4881825
## 45.28571 3.952400 1.3417302
## 45.42857 3.838326 1.1762939
## 45.57143 3.747159 1.0381074
## 45.71429 3.652201 0.8959113
## 45.85714 3.565498 0.7638063
## 46.00000 3.478612 0.6319445
## 46.14286 3.395222 0.5046035
## 
## $滋賀県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 13.48321 15.52493
## 44.42857 13.51056 15.58704
## 44.57143 13.49261 15.80146
## 44.71429 13.55854 15.91576
## 44.85714 13.58240 16.03289
## 45.00000 13.66423 16.16476
## 45.14286 13.72930 16.29123
## 45.28571 13.81574 16.42641
## 45.42857 13.89572 16.55775
## 45.57143 13.98220 16.69125
## 45.71429 14.06571 16.82200
## 45.85714 14.15054 16.95223
## 46.00000 14.23358 17.08024
## 46.14286 14.31624 17.20686
## 
## 
## $京都府
## $京都府$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 18.08589 17.98620 17.73286 14.74578 16.23750 16.15644 16.33614 17.84432
##  [9] 17.21202 17.43340 15.82527 16.36161 17.03074 16.90971
## 
## $京都府$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%      95%
## 44.28571 11.752154 8.399279
## 44.42857 11.080896 7.425450
## 44.57143 10.679233 6.945270
## 44.71429  7.546892 3.736028
## 44.85714  8.896209 5.009967
## 45.00000  8.675470 4.715284
## 45.14286  8.718051 4.685277
## 45.28571  9.847844 5.614766
## 45.42857  8.999886 4.652644
## 45.57143  9.054333 4.618722
## 45.71429  7.282530 2.760276
## 45.85714  7.658274 3.051008
## 46.00000  8.169722 3.478983
## 46.14286  7.893768 3.121016
## 
## $京都府$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 24.41962 27.77249
## 44.42857 24.89150 28.54694
## 44.57143 24.78648 28.52044
## 44.71429 21.94468 25.75554
## 44.85714 23.57878 27.46502
## 45.00000 23.63741 27.59760
## 45.14286 23.95424 27.98701
## 45.28571 25.84079 30.07387
## 45.42857 25.42416 29.77140
## 45.57143 25.81247 30.24808
## 45.71429 24.36801 28.89026
## 45.85714 25.06494 29.67221
## 46.00000 25.89176 30.58250
## 46.14286 25.92566 30.69841
## 
## 
## $大阪府
## $大阪府$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 229.6316 239.4637 222.9096 192.7408 279.1027 267.0558 277.4128 295.6460
##  [9] 308.2931 284.1320 251.0595 335.3551 342.8200 336.6935
## 
## $大阪府$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 205.5007 192.7266
## 44.42857 212.3098 197.9353
## 44.57143 194.3738 179.2679
## 44.71429 162.9088 147.1168
## 44.85714 247.6661 231.0246
## 45.00000 233.6381 215.9478
## 45.14286 241.7238 222.8312
## 45.28571 254.1134 232.1273
## 45.42857 263.0246 239.0609
## 45.57143 235.7231 210.0970
## 45.71429 199.6463 172.4298
## 45.85714 280.9443 252.1409
## 46.00000 285.4091 255.0176
## 46.14286 276.3077 244.3414
## 
## $大阪府$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 253.7625 266.5366
## 44.42857 266.6177 280.9921
## 44.57143 251.4454 266.5514
## 44.71429 222.5727 238.3648
## 44.85714 310.5393 327.1809
## 45.00000 300.4734 318.1637
## 45.14286 313.1018 331.9944
## 45.28571 337.1786 359.1647
## 45.42857 353.5617 377.5254
## 45.57143 332.5409 358.1670
## 45.71429 302.4728 329.6893
## 45.85714 389.7659 418.5692
## 46.00000 400.2309 430.6223
## 46.14286 397.0792 429.0455
## 
## 
## $兵庫県
## $兵庫県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 68.42479 59.62977 60.11839 54.25504 73.79952 78.68565 84.06038 77.91598
##  [9] 73.61863 73.85737 70.99247 80.54214 82.92956 85.55572
## 
## $兵庫県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 59.17101 54.27235
## 44.42857 49.79532 44.58928
## 44.57143 49.73569 44.23942
## 44.71429 43.35163 37.57971
## 44.85714 62.39916 56.36417
## 45.00000 66.80910 60.52204
## 45.14286 71.72602 65.19661
## 45.28571 63.29507 55.55523
## 45.42857 58.18060 50.00821
## 45.57143 57.64335 49.06017
## 45.71429 54.03794 45.06275
## 45.85714 62.87812 53.52735
## 46.00000 64.58346 54.87162
## 46.14286 66.55200 56.49204
## 
## $兵庫県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571  77.67857  82.57723
## 44.42857  69.46422  74.67027
## 44.57143  70.50108  75.99735
## 44.71429  65.15845  70.93037
## 44.85714  85.19989  91.23488
## 45.00000  90.56219  96.84926
## 45.14286  96.39473 102.92415
## 45.28571  92.53689 100.27673
## 45.42857  89.05665  97.22905
## 45.57143  90.07139  98.65457
## 45.71429  87.94700  96.92218
## 45.85714  98.20616 107.55693
## 46.00000 101.27566 110.98750
## 46.14286 104.55943 114.61939
## 
## 
## $奈良県
## $奈良県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 15.16326 13.67372 12.90375 14.39389 14.71870 14.41683 13.91192 13.75539
##  [9] 13.95312 14.10025 14.16584 14.07901 14.01875 14.01057
## 
## $奈良県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%      95%
## 44.28571 11.477455 9.526306
## 44.42857  9.793647 7.739661
## 44.57143  8.457216 6.103364
## 44.71429  9.868948 7.473588
## 44.85714 10.033613 7.553478
## 45.00000  9.445758 6.814232
## 45.14286  8.742193 6.005502
## 45.28571  8.385616 5.543029
## 45.42857  8.446083 5.530834
## 45.57143  8.439295 5.442565
## 45.71429  8.344278 5.262529
## 45.85714  8.097939 4.931753
## 46.00000  7.877102 4.625910
## 46.14286  7.725443 4.398296
## 
## $奈良県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 18.84907 20.80022
## 44.42857 17.55379 19.60778
## 44.57143 17.35028 19.70413
## 44.71429 18.91883 21.31419
## 44.85714 19.40379 21.88392
## 45.00000 19.38790 22.01942
## 45.14286 19.08166 21.81835
## 45.28571 19.12517 21.96775
## 45.42857 19.46016 22.37541
## 45.57143 19.76121 22.75794
## 45.71429 19.98740 23.06915
## 45.85714 20.06007 23.22626
## 46.00000 20.16040 23.41159
## 46.14286 20.29570 23.62285
## 
## 
## $和歌山県
## $和歌山県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 4.352392 3.088320 3.775615 3.895079 3.781258 2.927948 3.066149 3.390719
##  [9] 3.436049 3.436049 3.436049 3.436049 3.436049 3.436049
## 
## $和歌山県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%         95%
## 44.28571 2.4466441  1.43780209
## 44.42857 1.0328686 -0.05522139
## 44.57143 1.6512602  0.52669474
## 44.71429 1.7039862  0.54409176
## 44.85714 1.5254008  0.33122221
## 45.00000 0.6091348 -0.61837083
## 45.14286 0.6860435 -0.57390794
## 45.28571 1.0002360 -0.26520913
## 45.42857 1.0107083 -0.27318930
## 45.57143 0.9696437 -0.33599209
## 45.71429 0.9292518 -0.39776622
## 45.85714 0.8895005 -0.45856065
## 46.00000 0.8503602 -0.51842056
## 46.14286 0.8118036 -0.57738776
## 
## $和歌山県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 6.258140 7.266982
## 44.42857 5.143771 6.231861
## 44.57143 5.899970 7.024535
## 44.71429 6.086172 7.246066
## 44.85714 6.037115 7.231294
## 45.00000 5.246762 6.474267
## 45.14286 5.446254 6.706205
## 45.28571 5.781201 7.046646
## 45.42857 5.861389 7.145286
## 45.57143 5.902453 7.208089
## 45.71429 5.942845 7.269863
## 45.85714 5.982597 7.330658
## 46.00000 6.021737 7.390518
## 46.14286 6.060293 7.449485
## 
## 
## $鳥取県
## $鳥取県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 0.5854111 0.5355016 0.4966048 0.4662907 0.4426655 0.4242532 0.4099037
##  [8] 0.3987204 0.3900048 0.3832122 0.3779185 0.3737929 0.3705775 0.3680717
## 
## $鳥取県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%        95%
## 44.28571 -0.3604898 -0.8612194
## 44.42857 -0.4255904 -0.9343619
## 44.57143 -0.4743013 -0.9882679
## 44.71429 -0.5110957 -1.0284928
## 44.85714 -0.5390995 -1.0588145
## 45.00000 -0.5605448 -1.0818653
## 45.14286 -0.5770528 -1.0995161
## 45.28571 -0.5898174 -1.1131178
## 45.42857 -0.5997274 -1.1236600
## 45.57143 -0.6074506 -1.1318758
## 45.71429 -0.6134924 -1.1383136
## 45.85714 -0.6182377 -1.1433869
## 46.00000 -0.6219809 -1.1474097
## 46.14286 -0.6249484 -1.1506215
## 
## $鳥取県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 1.531312 2.032042
## 44.42857 1.496594 2.005365
## 44.57143 1.467511 1.981478
## 44.71429 1.443677 1.961074
## 44.85714 1.424430 1.944145
## 45.00000 1.409051 1.930372
## 45.14286 1.396860 1.919323
## 45.28571 1.387258 1.910559
## 45.42857 1.379737 1.903670
## 45.57143 1.373875 1.898300
## 45.71429 1.369329 1.894151
## 45.85714 1.365823 1.890973
## 46.00000 1.363136 1.888565
## 46.14286 1.361092 1.886765
## 
## 
## $島根県
## $島根県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 0.4653465 0.4653465 0.4653465 0.4653465 0.4653465 0.4653465 0.4653465
##  [8] 0.4653465 0.4653465 0.4653465 0.4653465 0.4653465 0.4653465 0.4653465
## 
## $島根県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%      95%
## 44.28571 -6.338959 -9.94094
## 44.42857 -6.338959 -9.94094
## 44.57143 -6.338959 -9.94094
## 44.71429 -6.338959 -9.94094
## 44.85714 -6.338959 -9.94094
## 45.00000 -6.338959 -9.94094
## 45.14286 -6.338959 -9.94094
## 45.28571 -6.338959 -9.94094
## 45.42857 -6.338959 -9.94094
## 45.57143 -6.338959 -9.94094
## 45.71429 -6.338959 -9.94094
## 45.85714 -6.338959 -9.94094
## 46.00000 -6.338959 -9.94094
## 46.14286 -6.338959 -9.94094
## 
## $島根県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 7.269652 10.87163
## 44.42857 7.269652 10.87163
## 44.57143 7.269652 10.87163
## 44.71429 7.269652 10.87163
## 44.85714 7.269652 10.87163
## 45.00000 7.269652 10.87163
## 45.14286 7.269652 10.87163
## 45.28571 7.269652 10.87163
## 45.42857 7.269652 10.87163
## 45.57143 7.269652 10.87163
## 45.71429 7.269652 10.87163
## 45.85714 7.269652 10.87163
## 46.00000 7.269652 10.87163
## 46.14286 7.269652 10.87163
## 
## 
## $岡山県
## $岡山県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 6.068403 6.091046 6.098541 6.101022 6.101844 6.102115 6.102205 6.102235
##  [9] 6.102245 6.102248 6.102249 6.102250 6.102250 6.102250
## 
## $岡山県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 3.473746 2.1002170
## 44.42857 3.201959 1.6725684
## 44.57143 3.091934 1.5003324
## 44.71429 3.016858 1.3841996
## 44.85714 2.952122 1.2847600
## 45.00000 2.891298 1.1915940
## 45.14286 2.832432 1.1015188
## 45.28571 2.774874 1.0134748
## 45.42857 2.718380 0.9270700
## 45.57143 2.662842 0.8421303
## 45.71429 2.608196 0.7585556
## 45.85714 2.554394 0.6762729
## 46.00000 2.501397 0.5952210
## 46.14286 2.449169 0.5153453
## 
## $岡山県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 8.663060 10.03659
## 44.42857 8.980133 10.50952
## 44.57143 9.105148 10.69675
## 44.71429 9.185187 10.81784
## 44.85714 9.251565 10.91893
## 45.00000 9.312933 11.01264
## 45.14286 9.371979 11.10289
## 45.28571 9.429597 11.19100
## 45.42857 9.486110 11.27742
## 45.57143 9.541655 11.36237
## 45.71429 9.596303 11.44594
## 45.85714 9.650105 11.52823
## 46.00000 9.703103 11.60928
## 46.14286 9.755331 11.68915
## 
## 
## $広島県
## $広島県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.874585 1.909659 1.968856 1.937709 1.912854 1.934653 1.943245 1.929850
##  [9] 1.928118 1.935599 1.934937 1.931104 1.932266 1.934056
## 
## $広島県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%        95%
## 44.28571 -1.647339  -3.511733
## 44.42857 -1.997764  -4.066229
## 44.57143 -2.130010  -4.299818
## 44.71429 -2.765239  -5.254830
## 44.85714 -3.331808  -6.108165
## 45.00000 -3.594562  -6.521552
## 45.14286 -3.891492  -6.980215
## 45.28571 -4.285824  -7.576204
## 45.42857 -4.608728  -8.069126
## 45.57143 -4.871944  -8.475640
## 45.71429 -5.157426  -8.911897
## 45.85714 -5.447796  -9.353950
## 46.00000 -5.707439  -9.751655
## 46.14286 -5.954676 -10.130718
## 
## $広島県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 5.396509  7.260903
## 44.42857 5.817083  7.885548
## 44.57143 6.067721  8.237530
## 44.71429 6.640657  9.130248
## 44.85714 7.157515  9.933872
## 45.00000 7.463868 10.390857
## 45.14286 7.777982 10.866705
## 45.28571 8.145524 11.435903
## 45.42857 8.464965 11.925363
## 45.57143 8.743143 12.346838
## 45.71429 9.027300 12.781771
## 45.85714 9.310004 13.216157
## 46.00000 9.571971 13.616187
## 46.14286 9.822788 13.998830
## 
## 
## $山口県
## $山口県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.915452 1.915452 1.915452 1.915452 1.915452 1.915452 1.915452 1.915452
##  [9] 1.915452 1.915452 1.915452 1.915452 1.915452 1.915452
## 
## $山口県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                   80%       95%
## 44.28571 -0.003529726 -1.019377
## 44.42857 -0.075389259 -1.129277
## 44.57143 -0.144743858 -1.235346
## 44.71429 -0.211838537 -1.337958
## 44.85714 -0.276880796 -1.437432
## 45.00000 -0.340048199 -1.534038
## 45.14286 -0.401494095 -1.628011
## 45.28571 -0.461352003 -1.719556
## 45.42857 -0.519739026 -1.808851
## 45.57143 -0.576758544 -1.896055
## 45.71429 -0.632502368 -1.981308
## 45.85714 -0.687052472 -2.064735
## 46.00000 -0.740482411 -2.146449
## 46.14286 -0.792858481 -2.226552
## 
## $山口県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 3.834434 4.850282
## 44.42857 3.906294 4.960182
## 44.57143 3.975649 5.066251
## 44.71429 4.042743 5.168863
## 44.85714 4.107786 5.268337
## 45.00000 4.170953 5.364943
## 45.14286 4.232399 5.458916
## 45.28571 4.292257 5.550461
## 45.42857 4.350644 5.639756
## 45.57143 4.407663 5.726960
## 45.71429 4.463407 5.812213
## 45.85714 4.517957 5.895640
## 46.00000 4.571387 5.977354
## 46.14286 4.623763 6.057456
## 
## 
## $徳島県
## $徳島県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.02260419 0.06751665 0.46192005 0.63682077 0.75522956 0.43354483
##  [7] 0.42833388 0.09802471 0.67178017 0.57510270 0.43008633 0.29937207
## [13] 0.58133167 0.58296382
## 
## $徳島県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -0.7097515 -1.626805
## 44.42857 -1.7473315 -2.708054
## 44.57143 -1.3716412 -2.342270
## 44.71429 -1.2152644 -2.195699
## 44.85714 -1.1151961 -2.105340
## 45.00000 -1.4550433 -2.454801
## 45.14286 -1.4782436 -2.487525
## 45.28571 -1.9077655 -2.969567
## 45.42857 -1.3686249 -2.448750
## 45.57143 -1.4896041 -2.582594
## 45.71429 -1.6586395 -2.764344
## 45.85714 -1.8130997 -2.931375
## 46.00000 -1.5546221 -2.685328
## 46.14286 -1.5762166 -2.719218
## 
## $徳島県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.754960 3.672014
## 44.42857 1.882365 2.843087
## 44.57143 2.295481 3.266110
## 44.71429 2.488906 3.469341
## 44.85714 2.625655 3.615799
## 45.00000 2.322133 3.321891
## 45.14286 2.334911 3.344193
## 45.28571 2.103815 3.165616
## 45.42857 2.712185 3.792310
## 45.57143 2.639810 3.732799
## 45.71429 2.518812 3.624517
## 45.85714 2.411844 3.530119
## 46.00000 2.717285 3.847991
## 46.14286 2.742144 3.885145
## 
## 
## $香川県
## $香川県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.2288528 0.7493941 0.7493941 0.7493941 0.7493941 0.7493941 0.7493941
##  [8] 0.7493941 0.7493941 0.7493941 0.7493941 0.7493941 0.7493941 0.7493941
## 
## $香川県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%        95%
## 44.28571 -0.1416946 -0.8672186
## 44.42857 -0.6854947 -1.4450789
## 44.57143 -0.6900826 -1.4520955
## 44.71429 -0.6946559 -1.4590897
## 44.85714 -0.6992147 -1.4660619
## 45.00000 -0.7037593 -1.4730122
## 45.14286 -0.7082897 -1.4799409
## 45.28571 -0.7128061 -1.4868481
## 45.42857 -0.7173085 -1.4937340
## 45.57143 -0.7217972 -1.5005989
## 45.71429 -0.7262722 -1.5074428
## 45.85714 -0.7307337 -1.5142661
## 46.00000 -0.7351818 -1.5210689
## 46.14286 -0.7396166 -1.5278513
## 
## $香川県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.599400 3.324924
## 44.42857 2.184283 2.943867
## 44.57143 2.188871 2.950884
## 44.71429 2.193444 2.957878
## 44.85714 2.198003 2.964850
## 45.00000 2.202547 2.971800
## 45.14286 2.207078 2.978729
## 45.28571 2.211594 2.985636
## 45.42857 2.216097 2.992522
## 45.57143 2.220585 2.999387
## 45.71429 2.225060 3.006231
## 45.85714 2.229522 3.013054
## 46.00000 2.233970 3.019857
## 46.14286 2.238405 3.026639
## 
## 
## $愛媛県
## $愛媛県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.2754894 1.0785277 0.2101412 0.6841925 0.1290706 0.6033757 0.5942661
##  [8] 0.6948557 0.2980236 0.5599344 0.2800868 0.5266180 0.2935342 0.5080597
## 
## $愛媛県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -0.3725469 -1.244965
## 44.42857 -0.6161846 -1.513311
## 44.57143 -1.5118494 -2.423416
## 44.71429 -1.0384823 -1.950411
## 44.85714 -1.5982720 -2.512672
## 45.00000 -1.1262613 -2.041876
## 45.14286 -1.1378455 -2.054770
## 45.28571 -1.0401929 -1.958672
## 45.42857 -1.4412713 -2.361998
## 45.57143 -1.1797654 -2.100707
## 45.71429 -1.4606826 -2.382190
## 45.85714 -1.2147969 -2.136646
## 46.00000 -1.4485191 -2.370706
## 46.14286 -1.2345144 -2.156977
## 
## $愛媛県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.923526 3.795943
## 44.42857 2.773240 3.670366
## 44.57143 1.932132 2.843698
## 44.71429 2.406867 3.318796
## 44.85714 1.856413 2.770813
## 45.00000 2.333013 3.248627
## 45.14286 2.326378 3.243302
## 45.28571 2.429904 3.348383
## 45.42857 2.037319 2.958046
## 45.57143 2.299634 3.220576
## 45.71429 2.020856 2.942364
## 45.85714 2.268033 3.189882
## 46.00000 2.035587 2.957775
## 46.14286 2.250634 3.173097
## 
## 
## $高知県
## $高知県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 0.1522674 0.1749162 0.1958380 0.2151643 0.2330169 0.2495081 0.2647418
##  [8] 0.2788138 0.2918127 0.3038204 0.3149124 0.3251586 0.3346234 0.3433665
## 
## $高知県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571 -1.550873 -2.452462
## 44.42857 -1.550003 -2.463120
## 44.57143 -1.547449 -2.470290
## 44.71429 -1.543645 -2.474702
## 44.85714 -1.538930 -2.476942
## 45.00000 -1.533572 -2.477478
## 45.14286 -1.527784 -2.476690
## 45.28571 -1.521733 -2.474885
## 45.42857 -1.515550 -2.472310
## 45.57143 -1.509339 -2.469167
## 45.71429 -1.503178 -2.465616
## 45.85714 -1.497129 -2.461789
## 46.00000 -1.491238 -2.457790
## 46.14286 -1.485538 -2.453702
## 
## $高知県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 1.855408 2.756996
## 44.42857 1.899835 2.812952
## 44.57143 1.939125 2.861966
## 44.71429 1.973973 2.905031
## 44.85714 2.004963 2.942975
## 45.00000 2.032588 2.976494
## 45.14286 2.057268 3.006174
## 45.28571 2.079361 3.032513
## 45.42857 2.099176 3.055936
## 45.57143 2.116979 3.076808
## 45.71429 2.133002 3.095441
## 45.85714 2.147446 3.112106
## 46.00000 2.160484 3.127037
## 46.14286 2.172271 3.140435
## 
## 
## $福岡県
## $福岡県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 17.19820 14.08014 13.24662 14.88534 19.77679 20.46022 18.56212 18.06666
##  [9] 15.76438 15.45022 16.71672 20.28146 20.55744 19.61067
## 
## $福岡県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%        95%
## 44.28571   3.615539  -3.574691
## 44.42857  -1.876470 -10.323388
## 44.57143  -3.994780 -13.121828
## 44.71429  -3.058641 -12.557609
## 44.85714   1.218495  -8.605674
## 45.00000   1.004721  -9.294397
## 45.14286  -2.247111 -13.262854
## 45.28571  -5.350288 -17.746469
## 45.42857  -9.494284 -22.865415
## 45.57143 -11.034641 -25.054882
## 45.71429 -10.568357 -25.012201
## 45.85714  -7.700051 -22.512570
## 46.00000  -8.293380 -23.566082
## 46.14286 -10.421499 -26.319570
## 
## $福岡県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 30.78087 37.97110
## 44.42857 30.03675 38.48366
## 44.57143 30.48802 39.61507
## 44.71429 32.82932 42.32828
## 44.85714 38.33509 48.15926
## 45.00000 39.91572 50.21484
## 45.14286 39.37135 50.38710
## 45.28571 41.48360 53.87978
## 45.42857 41.02305 54.39418
## 45.57143 41.93509 55.95533
## 45.71429 44.00179 58.44563
## 45.85714 48.26298 63.07550
## 46.00000 49.40826 64.68096
## 46.14286 49.64284 65.54091
## 
## 
## $佐賀県
## $佐賀県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.383199 1.383199 1.383199 1.383199 1.383199 1.383199 1.383199 1.383199
##  [9] 1.383199 1.383199 1.383199 1.383199 1.383199 1.383199
## 
## $佐賀県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -0.6161011 -1.674467
## 44.42857 -0.6914367 -1.789682
## 44.57143 -0.7641309 -1.900859
## 44.71429 -0.8344435 -2.008393
## 44.85714 -0.9025943 -2.112620
## 45.00000 -0.9687711 -2.213829
## 45.14286 -1.0331363 -2.312267
## 45.28571 -1.0958308 -2.408150
## 45.42857 -1.1569784 -2.501667
## 45.57143 -1.2166883 -2.592985
## 45.71429 -1.2750573 -2.682253
## 45.85714 -1.3321719 -2.769602
## 46.00000 -1.3881096 -2.855152
## 46.14286 -1.4429404 -2.939008
## 
## $佐賀県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 3.382499 4.440864
## 44.42857 3.457835 4.556080
## 44.57143 3.530529 4.667257
## 44.71429 3.600841 4.774790
## 44.85714 3.668992 4.879018
## 45.00000 3.735169 4.980227
## 45.14286 3.799534 5.078665
## 45.28571 3.862229 5.174548
## 45.42857 3.923376 5.268065
## 45.57143 3.983086 5.359383
## 45.71429 4.041455 5.448651
## 45.85714 4.098570 5.536000
## 46.00000 4.154507 5.621550
## 46.14286 4.209338 5.705406
## 
## 
## $長崎県
## $長崎県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1]  0.44413889 -0.09979553 -0.11934551 -0.13260926 -0.12583709 -0.12932192
##  [7] -0.03951982  0.22036456 -0.18548390 -0.15082876 -0.15325009 -0.15200046
## [13] -0.15263098 -0.17666571
## 
## $長崎県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571 -1.694763 -2.827030
## 44.42857 -2.479904 -3.739857
## 44.57143 -2.989758 -4.509262
## 44.71429 -3.290984 -4.962927
## 44.85714 -3.607207 -5.450133
## 45.00000 -3.877659 -5.861909
## 45.14286 -4.050569 -6.173891
## 45.28571 -4.181379 -6.511521
## 45.42857 -4.874417 -7.356588
## 45.57143 -5.144144 -7.787445
## 45.71429 -5.416859 -8.203245
## 45.85714 -5.680719 -8.607446
## 46.00000 -5.930370 -8.988921
## 46.14286 -6.195064 -9.381011
## 
## $長崎県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.583041 3.715307
## 44.42857 2.280313 3.540266
## 44.57143 2.751067 4.270571
## 44.71429 3.025766 4.697709
## 44.85714 3.355533 5.198459
## 45.00000 3.619015 5.603265
## 45.14286 3.971530 6.094851
## 45.28571 4.622108 6.952250
## 45.42857 4.503449 6.985620
## 45.57143 4.842486 7.485788
## 45.71429 5.110359 7.896745
## 45.85714 5.376718 8.303445
## 46.00000 5.625108 8.683659
## 46.14286 5.841732 9.027680
## 
## 
## $熊本県
## $熊本県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 8.777909 6.296244 6.972389 6.972389 6.972389 6.972389 6.972389 6.972389
##  [9] 6.972389 6.972389 6.972389 6.972389 6.972389 6.972389
## 
## $熊本県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                  80%       95%
## 44.28571  4.57345944  2.347758
## 44.42857  0.70303516 -2.257831
## 44.57143  1.22334353 -1.820017
## 44.71429  0.89042154 -2.329177
## 44.85714  0.57480097 -2.811877
## 45.00000  0.27403570 -3.271858
## 45.14286 -0.01379317 -3.712054
## 45.28571 -0.29022387 -4.134819
## 45.42857 -0.55651199 -4.542071
## 45.57143 -0.81369823 -4.935404
## 45.71429 -1.06265666 -5.316153
## 45.85714 -1.30412978 -5.685454
## 46.00000 -1.53875472 -6.044282
## 46.14286 -1.76708305 -6.393480
## 
## $熊本県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 12.98236 15.20806
## 44.42857 11.88945 14.85032
## 44.57143 12.72143 15.76479
## 44.71429 13.05436 16.27395
## 44.85714 13.36998 16.75665
## 45.00000 13.67074 17.21664
## 45.14286 13.95857 17.65683
## 45.28571 14.23500 18.07960
## 45.42857 14.50129 18.48685
## 45.57143 14.75848 18.88018
## 45.71429 15.00743 19.26093
## 45.85714 15.24891 19.63023
## 46.00000 15.48353 19.98906
## 46.14286 15.71186 20.33826
## 
## 
## $大分県
## $大分県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 0.9813007 0.8451203 0.5892030 0.5099573 0.5105285 0.5110690 0.5115806
##  [8] 0.5120647 0.5125229 0.5129565 0.5133669 0.5137553 0.5141228 0.5144706
## 
## $大分県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                 80%       95%
## 44.28571 -0.3948755 -1.123379
## 44.42857 -0.7419078 -1.582030
## 44.57143 -1.1678978 -2.098051
## 44.71429 -1.3114803 -2.275691
## 44.85714 -1.3203673 -2.289585
## 45.00000 -1.3282565 -2.301937
## 45.14286 -1.3352622 -2.312922
## 45.28571 -1.3414849 -2.322695
## 45.42857 -1.3470130 -2.331392
## 45.57143 -1.3519247 -2.339133
## 45.71429 -1.3562887 -2.346025
## 45.85714 -1.3601662 -2.352160
## 46.00000 -1.3636110 -2.357623
## 46.14286 -1.3666711 -2.362487
## 
## $大分県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.357477 3.085981
## 44.42857 2.432148 3.272270
## 44.57143 2.346304 3.276457
## 44.71429 2.331395 3.295606
## 44.85714 2.341424 3.310642
## 45.00000 2.350394 3.324075
## 45.14286 2.358423 3.336083
## 45.28571 2.365614 3.346824
## 45.42857 2.372059 3.356438
## 45.57143 2.377838 3.365046
## 45.71429 2.383023 3.372758
## 45.85714 2.387677 3.379671
## 46.00000 2.391857 3.385869
## 46.14286 2.395612 3.391429
## 
## 
## $宮崎県
## $宮崎県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] -0.005940314 -0.017153258 -0.017153258 -0.017153258 -0.017153258
##  [6] -0.017153258 -0.017153258 -0.017153258 -0.017153258 -0.017153258
## [11] -0.017153258 -0.017153258 -0.017153258 -0.017153258
## 
## $宮崎県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571 -2.705246 -4.134173
## 44.42857 -2.831190 -4.320851
## 44.57143 -3.157220 -4.819471
## 44.71429 -3.452445 -5.270979
## 44.85714 -3.724234 -5.686643
## 45.00000 -3.977413 -6.073848
## 45.14286 -4.215352 -6.437744
## 45.28571 -4.440510 -6.782094
## 45.42857 -4.654749 -7.109745
## 45.57143 -4.859520 -7.422913
## 45.71429 -5.055975 -7.723366
## 45.85714 -5.245053 -8.012536
## 46.00000 -5.427528 -8.291607
## 46.14286 -5.604046 -8.561568
## 
## $宮崎県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 2.693366 4.122292
## 44.42857 2.796884 4.286545
## 44.57143 3.122913 4.785164
## 44.71429 3.418139 5.236672
## 44.85714 3.689927 5.652337
## 45.00000 3.943107 6.039542
## 45.14286 4.181045 6.403437
## 45.28571 4.406203 6.747787
## 45.42857 4.620443 7.075438
## 45.57143 4.825213 7.388607
## 45.71429 5.021669 7.689060
## 45.85714 5.210747 7.978230
## 46.00000 5.393221 8.257300
## 46.14286 5.569739 8.527261
## 
## 
## $鹿児島県
## $鹿児島県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 1.724101 2.718524 2.747411 3.090015 3.405137 3.405137 3.405137 3.405137
##  [9] 3.405137 3.405137 3.405137 3.405137 3.405137 3.405137
## 
## $鹿児島県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%       95%
## 44.28571 -2.271284 -4.386314
## 44.42857 -2.042910 -4.563461
## 44.57143 -2.266400 -4.920552
## 44.71429 -2.059477 -4.785453
## 44.85714 -1.797978 -4.552340
## 45.00000 -1.810772 -4.571908
## 45.14286 -1.823536 -4.591428
## 45.28571 -1.836268 -4.610900
## 45.42857 -1.848969 -4.630325
## 45.57143 -1.861640 -4.649703
## 45.71429 -1.874280 -4.669035
## 45.85714 -1.886890 -4.688321
## 46.00000 -1.899471 -4.707560
## 46.14286 -1.912021 -4.726755
## 
## $鹿児島県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%       95%
## 44.28571 5.719486  7.834516
## 44.42857 7.479957 10.000508
## 44.57143 7.761223 10.415374
## 44.71429 8.239507 10.965483
## 44.85714 8.608252 11.362615
## 45.00000 8.621047 11.382183
## 45.14286 8.633810 11.401702
## 45.28571 8.646542 11.421175
## 45.42857 8.659244 11.440600
## 45.57143 8.671915 11.459978
## 45.71429 8.684555 11.479310
## 45.85714 8.697165 11.498595
## 46.00000 8.709745 11.517835
## 46.14286 8.722296 11.537029
## 
## 
## $沖縄県
## $沖縄県$mean
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##  [1] 27.01482 29.28913 28.93589 26.85084 28.11175 30.89359 27.53466 28.47618
##  [9] 28.47618 28.47618 28.47618 28.47618 28.47618 28.47618
## 
## $沖縄県$lower
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##                80%        95%
## 44.28571 15.305673  9.1072238
## 44.42857 16.497527  9.7260621
## 44.57143 15.146550  7.8469138
## 44.71429 12.131234  4.3391447
## 44.85714 12.517275  4.2620583
## 45.00000 14.470788  5.7770795
## 45.14286 10.323341  1.2122201
## 45.28571  9.543371 -0.4790505
## 45.42857  8.505539 -2.0662777
## 45.57143  7.519039 -3.5749989
## 45.71429  6.576933 -5.0158253
## 45.85714  5.673718 -6.3971732
## 46.00000  4.804942 -7.7258519
## 46.14286  3.966942 -9.0074625
## 
## $沖縄県$upper
## Time Series:
## Start = c(44, 3) 
## End = c(46, 2) 
## Frequency = 7 
##               80%      95%
## 44.28571 38.72397 44.92242
## 44.42857 42.08073 48.85220
## 44.57143 42.72523 50.02487
## 44.71429 41.57045 49.36254
## 44.85714 43.70623 51.96145
## 45.00000 47.31640 56.01011
## 45.14286 44.74598 53.85710
## 45.28571 47.40898 57.43140
## 45.42857 48.44681 59.01863
## 45.57143 49.43331 60.52735
## 45.71429 50.37542 61.96818
## 45.85714 51.27863 63.34952
## 46.00000 52.14741 64.67820
## 46.14286 52.98541 65.95981